Upload 1525 files

.gitattributes

@@ -33,3 +33,41 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/affine.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/amp_training_a100.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/amp_training_v100.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/arch_modules.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/auto3dseg.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/brats_distributed.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/BTCV_organs.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/cache_dataset.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/coplenet.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/deepedit.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/deepgrow_scheme.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/detection.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/dints-overview.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/fast_training.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/gmm_feature_set_comparison_s.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/invert_transforms.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/maisi_infer.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/maisi_train.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/matshow3d.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/medical_transforms.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/metrics_report.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/MONAI_bundle_cloud.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/MONAI_map_cloud.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/nsight_comparison.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/nuclick.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/pathology-meta.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/pathology.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/postprocessing_transforms.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/sliding_window.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/ssl_overview.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/swin_unetr.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/tta.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/unet-pipe.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/UNETR.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/vista2d.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/docs/images/workflows.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/tests/testing_data/ultrasound_confidence_map/femur_input.png filter=lfs diff=lfs merge=lfs -text
+MONAI/source/tests/testing_data/ultrasound_confidence_map/neck_input.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,18 @@
+FROM python:3.10
+
+RUN useradd -m -u 1000 user && python -m pip install --upgrade pip
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+ENV MCP_TRANSPORT=http
+ENV MCP_PORT=7860
+
+EXPOSE 7860
+
+CMD ["python", "MONAI/mcp_output/start_mcp.py"]

MONAI/mcp_output/README_MCP.md

@@ -0,0 +1,51 @@
+# MONAI: Medical Open Network for AI
+
+## Project Introduction
+
+MONAI (Medical Open Network for AI) is an open-source, PyTorch-based framework designed to facilitate deep learning in healthcare imaging. It provides domain-specific implementations for medical image analysis tasks such as segmentation, classification, detection, and registration. MONAI aims to support researchers and developers by offering optimized and standardized tools for deep learning model development in medical imaging.
+
+## Installation Method
+
+To install MONAI, ensure that you have Python and pip installed. MONAI requires several dependencies, including `numpy`, `torch`, and `nibabel`. Optional dependencies include `matplotlib` and `scipy` for additional functionalities.
+
+To install MONAI, use the following pip command:
+
+```
+pip install monai
+```
+
+Ensure that your environment meets the necessary requirements for PyTorch and other dependencies.
+
+## Quick Start
+
+Here's a quick example of how to use MONAI for a simple medical imaging task:
+
+1. Import the necessary modules from MONAI.
+2. Load your medical imaging data using MONAI's data handling utilities.
+3. Apply preprocessing transforms using MONAI's transform system.
+4. Define and train a neural network using MONAI's network architectures and training engines.
+
+For detailed examples and tutorials, refer to the [MONAI documentation](https://docs.monai.io).
+
+## Available Tools and Endpoints List
+
+- **Auto3DSeg**: Modules for automated 3D segmentation tasks, including `AutoRunner`, `BundleGen`, and `DataAnalyzer`.
+- **Transforms**: Compose multiple transforms together using `Compose` and `SomeOf`.
+- **Networks**: Network architectures for medical imaging, such as `UNet` and `ResNet`.
+- **Bundle System**: CLI for managing MONAI bundles, accessible via `monai-bundle`.
+
+## Common Issues and Notes
+
+- **Dependencies**: Ensure all required dependencies are installed. Use the provided `requirements.txt` for guidance.
+- **Environment**: MONAI is designed to work with PyTorch, so ensure your environment is compatible with the PyTorch version you are using.
+- **Performance**: For optimal performance, consider using a GPU-enabled environment, as MONAI supports multi-GPU and multi-node data parallelism.
+
+## Reference Links or Documentation
+
+For more detailed information, tutorials, and API references, please visit the following resources:
+
+- [MONAI GitHub Repository](https://github.com/Project-MONAI/MONAI)
+- [MONAI Documentation](https://docs.monai.io)
+- [MONAI Tutorials](https://github.com/Project-MONAI/tutorials)
+
+For any issues or contributions, please refer to the [CONTRIBUTING.md](https://github.com/Project-MONAI/MONAI/blob/main/CONTRIBUTING.md) and [CODE_OF_CONDUCT.md](https://github.com/Project-MONAI/MONAI/blob/main/CODE_OF_CONDUCT.md) files in the repository.

MONAI/mcp_output/diff_report.md

@@ -0,0 +1,61 @@
+# MONAI Project Difference Report
+
+**Date:** February 3, 2026  
+**Time:** 13:35:35  
+**Repository:** MONAI  
+**Project Type:** Python Library  
+**Intrusiveness:** None  
+**Workflow Status:** Success  
+**Test Status:** Failed  
+
+## Project Overview
+
+The MONAI project is a Python library designed to facilitate the development of deep learning models in medical imaging. It provides a comprehensive set of tools and functionalities to streamline the process of creating, training, and deploying models for various medical imaging tasks.
+
+## Difference Analysis
+
+### New Files Added
+
+Since the last update, the MONAI project has introduced 8 new files. These files are likely to include new features, enhancements, or additional modules that expand the library's capabilities. However, the specific content and purpose of these files have not been detailed in this report.
+
+### Modified Files
+
+There have been no modifications to existing files in this update. This suggests that the recent changes are entirely additive, focusing on expanding the library's functionality without altering the current codebase.
+
+## Technical Analysis
+
+### Workflow Status
+
+The workflow status is marked as "success," indicating that the integration and deployment processes for the new files were executed without any errors or interruptions. This suggests that the new additions were integrated smoothly into the existing framework.
+
+### Test Status
+
+Despite the successful workflow, the test status is marked as "failed." This indicates that one or more tests did not pass, which could be due to issues with the new files or their integration with existing components. The failure in testing suggests potential bugs or compatibility issues that need to be addressed.
+
+## Recommendations and Improvements
+
+1. **Investigate Test Failures:** Conduct a thorough analysis of the failed tests to identify the root causes. This may involve reviewing the new files for errors, ensuring compatibility with existing modules, and verifying that all dependencies are correctly configured.
+
+2. **Enhance Testing Framework:** Consider expanding the test coverage to include the new functionalities introduced by the recent files. This will help in identifying potential issues early in the development cycle.
+
+3. **Documentation Update:** Ensure that the documentation is updated to reflect the new features and functionalities. This will aid users in understanding and utilizing the new capabilities effectively.
+
+4. **Community Feedback:** Engage with the user community to gather feedback on the new features. This can provide valuable insights into potential improvements and user needs.
+
+## Deployment Information
+
+The deployment of the new files was successful, as indicated by the workflow status. However, due to the test failures, it is recommended to hold off on any production deployment until the issues are resolved. This will prevent potential disruptions or errors in user environments.
+
+## Future Planning
+
+1. **Bug Fixes and Patches:** Prioritize resolving the test failures and releasing patches to address any identified issues.
+
+2. **Feature Expansion:** Continue to expand the library's capabilities by introducing new features and enhancements based on user feedback and technological advancements in medical imaging.
+
+3. **Community Engagement:** Strengthen community engagement through forums, webinars, and collaborative projects to foster a supportive ecosystem around the MONAI library.
+
+4. **Performance Optimization:** Explore opportunities to optimize the library's performance, ensuring it remains efficient and scalable for various medical imaging applications.
+
+## Conclusion
+
+The recent update to the MONAI project introduces new functionalities that enhance the library's capabilities. However, the test failures highlight the need for further investigation and resolution before these changes can be fully integrated into production environments. By addressing these issues and implementing the recommended improvements, the MONAI project can continue to provide valuable tools for the medical imaging community.

MONAI/mcp_output/mcp_plugin/adapter.py

@@ -0,0 +1,181 @@
+import os
+import sys
+
+# Path settings
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+# Import statements
+try:
+    from monai.bundle import ConfigWorkflow, ConfigParser
+    from monai.apps.auto3dseg import AutoRunner, BundleGen, AlgoEnsemble
+    from monai.data import MetaTensor
+    from monai.transforms import Compose, MapTransform, InvertibleTransform
+    from monai.engines import Workflow, IterationEvents
+except ImportError as e:
+    print(f"Import failed: {e}. Please ensure all dependencies are correctly installed.")
+
+class Adapter:
+    """
+    Adapter class for the MCP plugin, providing methods to interact with MONAI's core functionalities.
+    """
+
+    def __init__(self):
+        self.mode = "import"
+
+    # -------------------- Bundle Module --------------------
+
+    def create_config_workflow(self, config_path):
+        """
+        Create a ConfigWorkflow instance.
+
+        :param config_path: Path to the configuration file.
+        :return: Dictionary with status and ConfigWorkflow instance or error message.
+        """
+        try:
+            workflow = ConfigWorkflow(config_path)
+            return {"status": "success", "workflow": workflow}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    def parse_config(self, config_path):
+        """
+        Parse a configuration file using ConfigParser.
+
+        :param config_path: Path to the configuration file.
+        :return: Dictionary with status and parsed configuration or error message.
+        """
+        try:
+            config = ConfigParser(config_path)
+            return {"status": "success", "config": config}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    # -------------------- Auto3DSeg Module --------------------
+
+    def run_auto_runner(self, config):
+        """
+        Run the AutoRunner with the given configuration.
+
+        :param config: Configuration for the AutoRunner.
+        :return: Dictionary with status and AutoRunner instance or error message.
+        """
+        try:
+            runner = AutoRunner(config)
+            runner.run()
+            return {"status": "success", "runner": runner}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    def generate_bundle(self, template_path):
+        """
+        Generate a bundle using BundleGen.
+
+        :param template_path: Path to the template.
+        :return: Dictionary with status and BundleGen instance or error message.
+        """
+        try:
+            bundle_gen = BundleGen(template_path)
+            return {"status": "success", "bundle_gen": bundle_gen}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    def create_algo_ensemble(self, models):
+        """
+        Create an algorithm ensemble using AlgoEnsemble.
+
+        :param models: List of models to include in the ensemble.
+        :return: Dictionary with status and AlgoEnsemble instance or error message.
+        """
+        try:
+            ensemble = AlgoEnsemble(models)
+            return {"status": "success", "ensemble": ensemble}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    # -------------------- Data Module --------------------
+
+    def create_meta_tensor(self, data, meta=None):
+        """
+        Create a MetaTensor instance.
+
+        :param data: Data for the MetaTensor.
+        :param meta: Metadata for the MetaTensor.
+        :return: Dictionary with status and MetaTensor instance or error message.
+        """
+        try:
+            meta_tensor = MetaTensor(data, meta)
+            return {"status": "success", "meta_tensor": meta_tensor}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    # -------------------- Transforms Module --------------------
+
+    def compose_transforms(self, transforms):
+        """
+        Compose a series of transforms.
+
+        :param transforms: List of transforms to compose.
+        :return: Dictionary with status and Compose instance or error message.
+        """
+        try:
+            composed = Compose(transforms)
+            return {"status": "success", "composed": composed}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    def map_transform(self, data, transform):
+        """
+        Apply a MapTransform to the data.
+
+        :param data: Data to transform.
+        :param transform: Transform to apply.
+        :return: Dictionary with status and transformed data or error message.
+        """
+        try:
+            mapped_data = MapTransform(data, transform)
+            return {"status": "success", "mapped_data": mapped_data}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    def invertible_transform(self, data, transform):
+        """
+        Apply an InvertibleTransform to the data.
+
+        :param data: Data to transform.
+        :param transform: Transform to apply.
+        :return: Dictionary with status and transformed data or error message.
+        """
+        try:
+            inverted_data = InvertibleTransform(data, transform)
+            return {"status": "success", "inverted_data": inverted_data}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    # -------------------- Engines Module --------------------
+
+    def create_workflow(self, config):
+        """
+        Create a Workflow instance.
+
+        :param config: Configuration for the workflow.
+        :return: Dictionary with status and Workflow instance or error message.
+        """
+        try:
+            workflow = Workflow(config)
+            return {"status": "success", "workflow": workflow}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+
+    def handle_iteration_events(self, workflow):
+        """
+        Handle iteration events in a workflow.
+
+        :param workflow: Workflow instance.
+        :return: Dictionary with status and event handling result or error message.
+        """
+        try:
+            events = IterationEvents(workflow)
+            return {"status": "success", "events": events}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}

MONAI/mcp_output/mcp_plugin/main.py

@@ -0,0 +1,13 @@
+"""
+MCP Service Auto-Wrapper - Auto-generated
+"""
+from mcp_service import create_app
+
+def main():
+    """Main entry point"""
+    app = create_app()
+    return app
+
+if __name__ == "__main__":
+    app = main()
+    app.run()

MONAI/mcp_output/mcp_plugin/mcp_service.py

@@ -0,0 +1,139 @@
+import os
+import sys
+
+# Add the local source directory to sys.path
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+if source_path not in sys.path:
+    sys.path.insert(0, source_path)
+
+from fastmcp import FastMCP
+
+# Import core modules from the local source directory
+from monai.data import Dataset, DataLoader
+from monai.transforms import Compose, LoadImage, NormalizeIntensity
+from monai.networks.nets import UNet
+from monai.engines import SupervisedTrainer
+from monai.metrics import DiceMetric
+
+# Create the FastMCP service application
+mcp = FastMCP("monai_service")
+
+@mcp.tool(name="load_dataset", description="Load a dataset using MONAI's Dataset class")
+def load_dataset(data_dir: str) -> dict:
+    """
+    Load a dataset from the specified directory.
+
+    Parameters:
+    - data_dir (str): The directory containing the dataset.
+
+    Returns:
+    - dict: A dictionary containing success status and the dataset object or error message.
+    """
+    try:
+        dataset = Dataset(data_dir)
+        return {"success": True, "result": dataset}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+@mcp.tool(name="create_dataloader", description="Create a DataLoader for the dataset")
+def create_dataloader(dataset: Dataset, batch_size: int) -> dict:
+    """
+    Create a DataLoader for the given dataset.
+
+    Parameters:
+    - dataset (Dataset): The dataset to load.
+    - batch_size (int): The number of samples per batch.
+
+    Returns:
+    - dict: A dictionary containing success status and the DataLoader object or error message.
+    """
+    try:
+        dataloader = DataLoader(dataset, batch_size=batch_size)
+        return {"success": True, "result": dataloader}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+@mcp.tool(name="apply_transforms", description="Apply transforms to the dataset")
+def apply_transforms(dataset: Dataset) -> dict:
+    """
+    Apply a series of transforms to the dataset.
+
+    Parameters:
+    - dataset (Dataset): The dataset to transform.
+
+    Returns:
+    - dict: A dictionary containing success status and the transformed dataset or error message.
+    """
+    try:
+        transforms = Compose([LoadImage(), NormalizeIntensity()])
+        transformed_dataset = [transforms(item) for item in dataset]
+        return {"success": True, "result": transformed_dataset}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+@mcp.tool(name="initialize_unet", description="Initialize a UNet model")
+def initialize_unet(spatial_dims: int, in_channels: int, out_channels: int) -> dict:
+    """
+    Initialize a UNet model with the specified parameters.
+
+    Parameters:
+    - spatial_dims (int): The number of spatial dimensions.
+    - in_channels (int): The number of input channels.
+    - out_channels (int): The number of output channels.
+
+    Returns:
+    - dict: A dictionary containing success status and the UNet model or error message.
+    """
+    try:
+        model = UNet(spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels)
+        return {"success": True, "result": model}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+@mcp.tool(name="train_model", description="Train a model using MONAI's SupervisedTrainer")
+def train_model(model, dataloader: DataLoader, max_epochs: int) -> dict:
+    """
+    Train a model using the specified dataloader and number of epochs.
+
+    Parameters:
+    - model: The model to train.
+    - dataloader (DataLoader): The DataLoader for training data.
+    - max_epochs (int): The maximum number of training epochs.
+
+    Returns:
+    - dict: A dictionary containing success status and training results or error message.
+    """
+    try:
+        trainer = SupervisedTrainer(max_epochs=max_epochs, train_data_loader=dataloader, network=model)
+        trainer.run()
+        return {"success": True, "result": "Training completed"}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+@mcp.tool(name="calculate_dice", description="Calculate Dice metric for model evaluation")
+def calculate_dice(predictions, targets) -> dict:
+    """
+    Calculate the Dice metric for the given predictions and targets.
+
+    Parameters:
+    - predictions: The model predictions.
+    - targets: The ground truth targets.
+
+    Returns:
+    - dict: A dictionary containing success status and the Dice score or error message.
+    """
+    try:
+        dice_metric = DiceMetric()
+        dice_score = dice_metric(predictions, targets)
+        return {"success": True, "result": dice_score}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+def create_app() -> FastMCP:
+    """
+    Create and return the FastMCP application instance.
+
+    Returns:
+    - FastMCP: The FastMCP application instance.
+    """
+    return mcp

MONAI/mcp_output/requirements.txt

@@ -0,0 +1,7 @@
+fastmcp
+fastapi
+uvicorn[standard]
+pydantic>=2.0.0
+torch>=2.4.1; platform_system != "Windows"
+numpy>=1.24,<3.0
+nibabel

MONAI/mcp_output/start_mcp.py

@@ -0,0 +1,30 @@
+
+"""
+MCP Service Startup Entry
+"""
+import sys
+import os
+
+project_root = os.path.dirname(os.path.abspath(__file__))
+mcp_plugin_dir = os.path.join(project_root, "mcp_plugin")
+if mcp_plugin_dir not in sys.path:
+    sys.path.insert(0, mcp_plugin_dir)
+
+from mcp_service import create_app
+
+def main():
+    """Start FastMCP service"""
+    app = create_app()
+    # Use environment variable to configure port, default 8000
+    port = int(os.environ.get("MCP_PORT", "8000"))
+    
+    # Choose transport mode based on environment variable
+    transport = os.environ.get("MCP_TRANSPORT", "stdio")
+    if transport == "http":
+        app.run(transport="http", host="0.0.0.0", port=port)
+    else:
+        # Default to STDIO mode
+        app.run()
+
+if __name__ == "__main__":
+    main()

MONAI/mcp_output/workflow_summary.json

@@ -0,0 +1,228 @@
+{
+  "repository": {
+    "name": "MONAI",
+    "url": "https://github.com/Project-MONAI/MONAI",
+    "local_path": "/export/zxcpu1/shiweijie/code/ghh/Code2MCP/workspace/MONAI",
+    "description": "Python library",
+    "features": "Basic functionality",
+    "tech_stack": "Python",
+    "stars": 0,
+    "forks": 0,
+    "language": "Python",
+    "last_updated": "",
+    "complexity": "complex",
+    "intrusiveness_risk": "medium"
+  },
+  "execution": {
+    "start_time": 1770096773.8238096,
+    "end_time": 1770096879.8965826,
+    "duration": 106.07277607917786,
+    "status": "success",
+    "workflow_status": "success",
+    "nodes_executed": [
+      "download",
+      "analysis",
+      "env",
+      "generate",
+      "run",
+      "review",
+      "finalize"
+    ],
+    "total_files_processed": 35,
+    "environment_type": "unknown",
+    "llm_calls": 0,
+    "deepwiki_calls": 0
+  },
+  "tests": {
+    "original_project": {
+      "passed": false,
+      "details": {},
+      "test_coverage": "100%",
+      "execution_time": 0,
+      "test_files": []
+    },
+    "mcp_plugin": {
+      "passed": true,
+      "details": {},
+      "service_health": "healthy",
+      "startup_time": 0,
+      "transport_mode": "stdio",
+      "fastmcp_version": "unknown",
+      "mcp_version": "unknown"
+    }
+  },
+  "analysis": {
+    "structure": {
+      "packages": [
+        "source.monai",
+        "source.monai._extensions",
+        "source.monai.apps",
+        "source.monai.auto3dseg",
+        "source.monai.bundle",
+        "source.monai.config",
+        "source.monai.data",
+        "source.monai.engines",
+        "source.monai.fl",
+        "source.monai.handlers",
+        "source.monai.inferers",
+        "source.monai.losses",
+        "source.monai.metrics",
+        "source.monai.networks",
+        "source.monai.optimizers",
+        "source.monai.transforms",
+        "source.monai.utils",
+        "source.monai.visualize",
+        "source.tests",
+        "source.tests.apps",
+        "source.tests.bundle",
+        "source.tests.config",
+        "source.tests.engines",
+        "source.tests.fl",
+        "source.tests.handlers",
+        "source.tests.inferers",
+        "source.tests.integration",
+        "source.tests.losses",
+        "source.tests.metrics",
+        "source.tests.networks",
+        "source.tests.optimizers",
+        "source.tests.profile_subclass",
+        "source.tests.transforms",
+        "source.tests.utils",
+        "source.tests.visualize"
+      ]
+    },
+    "dependencies": {
+      "has_environment_yml": false,
+      "has_requirements_txt": true,
+      "pyproject": true,
+      "setup_cfg": true,
+      "setup_py": true
+    },
+    "entry_points": {
+      "imports": [],
+      "cli": [],
+      "modules": []
+    },
+    "risk_assessment": {
+      "import_feasibility": 0.8,
+      "intrusiveness_risk": "medium",
+      "complexity": "complex"
+    },
+    "deepwiki_analysis": {
+      "repo_url": "https://github.com/Project-MONAI/MONAI",
+      "repo_name": "MONAI",
+      "content": "Project-MONAI/MONAI\nMONAI Overview\nCore Architecture\nCore Utilities and Module System\nTransform System\nTransform Architecture and Base Classes\nSpatial Transforms\nIntensity and Utility Transforms\nCrop, Pad, and Post-processing Transforms\nDictionary-Based Transforms\nInvertible Transforms and MetaTensor\nLazy Transform Execution\nData Loading and Processing\nDataset System and Caching Strategies\nImage I/O and Readers\nModels and Training\nNetwork Architectures and Components\nTraining Engines and Workflows\nAdvanced Features\nBundle System\nBundle Format and Structure\nBundle Scripts and Workflows\nAuto3DSeg Pipeline\nDevelopment and Infrastructure\nProject Setup and Dependencies\nCI/CD Workflows and Testing\nPackaging and Distribution\nMONAI Overview\ndocs/images/MONAI_arch.png\ndocs/images/MONAI_bundle_cloud.png\ndocs/images/MONAI_clouds.png\ndocs/images/MONAI_map_cloud.png\ndocs/source/bundle_intro.rst\ndocs/source/index.rst\ndocs/source/mb_specification.rst\ntests/testing_data/metadata.json\nPurpose and Scope\nThis document provides a high-level introduction to the MONAI framework, its architecture, and core concepts. It covers the overall organization of the codebase, key modules, and how they interact to support deep learning workflows for medical imaging.\nFor detailed information about specific subsystems:\nCore utilities and module management: seeCore Utilities and Module System\nTransform system architecture: seeTransform System\nData loading and caching: seeData Loading and Processing\nNeural network architectures: seeNetwork Architectures and Components\nTraining workflows: seeTraining Engines and Workflows\nBundle format and distribution: seeBundle System\nDevelopment setup and CI/CD: seeDevelopment and Infrastructure\nWhat is MONAI?\nMONAI(Medical Open Network for AI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It is part of the PyTorch Ecosystem and provides domain-specific implementations for medical image analysis tasks including segmentation, classification, detection, and registration.\nCore Ambitions:\nDevelop a collaborative community of academic, industrial, and clinical researchers\nCreate state-of-the-art, end-to-end training workflows for healthcare imaging\nProvide researchers with optimized and standardized tools for deep learning model development\nKey Features:\nFlexible preprocessing for multi-dimensional medical imaging data\nCompositional and portable APIs for integration into existing workflows\nDomain-specific network architectures, loss functions, and evaluation metrics\nCustomizable design supporting varying levels of user expertise\nMulti-GPU and multi-node data parallelism support\nSources:README.md1-26docs/source/index.rst1-44\nArchitecture Overview\nMONAI's architecture is organized into distinct layers, each providing specific functionality for medical imaging workflows. The diagram below maps high-level system components to their corresponding code modules.\nmonai/Core UtilitiesHigh-Level SystemsTraining InfrastructureNeural NetworksCore Data Pipelinesupportssupportssupportsmonai.utilsmodule, misc, enumsmonai.dataDataset, DataLoader,CacheDataset, ImageReadermonai.transformsCompose, Spatial, Intensity,MapTransform, InvertibleTransformmonai.enginesTrainer, Evaluator, Workflowmonai.networksnets/, blocks/, layers/monai.lossesDiceLoss, FocalLoss, etc.monai.metricsDiceMetric, ConfusionMatrixMetricmonai.handlersCheckpointSaver, StatsHandler,ValidationHandlermonai.bundleConfigWorkflow, scripts/monai.apps.auto3dsegAutoRunner, BundleGenmonai.configdeviceconfig, type_definitions\nCore Utilities\nHigh-Level Systems\nTraining Infrastructure\nNeural Networks\nCore Data Pipeline\nmonai.utilsmodule, misc, enums\nmonai.dataDataset, DataLoader,CacheDataset, ImageReader\nmonai.transformsCompose, Spatial, Intensity,MapTransform, InvertibleTransform\nmonai.enginesTrainer, Evaluator, Workflow\nmonai.networksnets/, blocks/, layers/\nmonai.lossesDiceLoss, FocalLoss, etc.\nmonai.metricsDiceMetric, ConfusionMatrixMetric\nmonai.handlersCheckpointSaver, StatsHandler,ValidationHandler\nmonai.bundleConfigWorkflow, scripts/\nmonai.apps.auto3dsegAutoRunner, BundleGen\nmonai.configdeviceconfig, type_definitions\nSources:README.md20-44docs/source/index.rst14-34\nCore Module Organization\nThe MONAI codebase is organized into the following primary modules located under themonai/directory:\nData Pipeline (monai.data)\nHandles medical image loading, caching, and batching:\nDataset: Base dataset class and specialized variants (CacheDataset,PersistentDataset,LMDBDataset)\nCacheDataset\nPersistentDataset\nLMDBDataset\nDataLoader: PyTorch-compatible data loaders with custom collation\nImageReader: Format-specific readers (ITKReader,NibabelReader,PydicomReader)\nImageReader\nNibabelReader\nPydicomReader\nDecathalon dataset utilities\nKey Classes:Dataset,CacheDataset,DataLoader,ImageReader\nCacheDataset\nImageReader\nTransforms (monai.transforms)\nmonai.transforms\nComposable preprocessing and augmentation operations:\nBase Classes:Transform,MapTransform,RandomizableTransform,InvertibleTransform,LazyTransform\nMapTransform\nRandomizableTransform\nInvertibleTransform\nLazyTransform\nCategories:Spatial (resampling, rotation), Intensity (normalization, scaling), Crop/Pad, Post-processing\nDictionary Pattern:Transforms ending with 'd' suffix operate on dictionaries (e.g.,LoadImaged,Spacingd)\nKey Classes:Compose,LoadImage,Spacing,NormalizeIntensity,RandCropByPosNegLabel\nNormalizeIntensity\nRandCropByPosNegLabel\nNetworks (monai.networks)\nmonai.networks\nNeural network architectures and building blocks:\nnets/: Complete architectures (UNet, ResNet, DenseNet, ViT, UNETR, etc.)\nblocks/: Reusable network components (Convolution, Residual, Attention blocks)\nlayers/: Low-level layer implementations (Act, Norm, Conv, Dropout)\nKey Classes:UNet,DynUNet,SegResNet,UNETR,SwinUNETR\nTraining & Evaluation (monai.engines,monai.metrics,monai.handlers)\nmonai.engines\nmonai.metrics\nmonai.handlers\nEvent-driven training infrastructure built on PyTorch Ignite:\nEngines:Workflow,SupervisedTrainer,SupervisedEvaluator,EnsembleEvaluator\nSupervisedTrainer\nSupervisedEvaluator\nEnsembleEvaluator\nMetrics:DiceMetric,HausdorffDistanceMetric,ConfusionMatrixMetric,ROCAUCMetric\nHausdorffDistanceMetric\nConfusionMatrixMetric\nROCAUCMetric\nHandlers:CheckpointSaver,StatsHandler,TensorBoardHandler,ValidationHandler\nCheckpointSaver\nStatsHandler\nTensorBoardHandler\nValidationHandler\nKey Classes:SupervisedTrainer,SupervisedEvaluator,DiceMetric,CheckpointSaver\nSupervisedTrainer\nSupervisedEvaluator\nCheckpointSaver\nBundle System (monai.bundle)\nmonai.bundle\nStandardized model packaging and distribution:\nConfiguration-based workflows (ConfigWorkflow)\nConfigWorkflow\nBundle scripts for download, load, run, verify operations\nModel export utilities (TorchScript, ONNX, TensorRT)\nKey Classes:ConfigWorkflow,ConfigParser\nConfigWorkflow\nConfigParser\nAuto3DSeg (monai.apps.auto3dseg)\nmonai.apps.auto3dseg\nAutomated pipeline for 3D segmentation tasks:\nAutoRunner: Orchestrates the complete automated workflow\nBundleGen: Generates algorithm bundles from templates\nAlgoEnsemble: Combines multiple models for inference\nAlgoEnsemble\nKey Classes:AutoRunner,BundleGen,AlgoEnsemble\nAlgoEnsemble\nSources:README.md27-44docs/source/index.rst27-34\nKey Abstractions and Design Patterns\nMONAI'sMetaTensorextends PyTorch tensors with metadata tracking capabilities. It stores:\nAffine transformations for spatial coordinates\nApplied operations history (applied_operations)\napplied_operations\nPending lazy operations (pending_operations)\npending_operations\nOriginal image metadata (spacing, orientation, etc.)\nThis enables invertible transforms and preserves provenance throughout the pipeline.\nKey Class:monai.data.MetaTensor\nmonai.data.MetaTensor\nTransform Composition\nTransforms follow a functional composition pattern where operations are chained:\nRaw ImageComposeLoadImageEnsureChannelFirstSpacingNormalizeIntensityRandSpatialCropPreprocessed Tensor\nEnsureChannelFirst\nNormalizeIntensity\nRandSpatialCrop\nPreprocessed Tensor\nDictionary-based transforms (suffix 'd') operate on dictionaries of data items, enabling coordinated transformations across images, labels, and metadata.\nKey Classes:Compose,MapTransform,InvertibleTransform\nMapTransform\nInvertibleTransform\nEvent-Driven Training\nTraining engines use an event system built on PyTorch Ignite:\nEngine / WorkflowEPOCH_STARTEDITERATION_STARTEDFORWARD_COMPLETEDBACKWARD_COMPLETEDITERATION_COMPLETEDEPOCH_COMPLETEDMetrics UpdateStatsHandlerCheckpointSaverValidationHandler\nEngine / Workflow\nEPOCH_STARTED\nITERATION_STARTED\nFORWARD_COMPLETED\nBACKWARD_COMPLETED\nITERATION_COMPLETED\nEPOCH_COMPLETED\nMetrics Update\nStatsHandler\nCheckpointSaver\nValidationHandler\nHandlers attach to events to perform actions like logging, checkpointing, or validation.\nKey Classes:Workflow,IterationEvents,Handler\nIterationEvents\nBundle Configuration System\nBundles use JSON/YAML configurations with a reference syntax for defining workflows:\n@network_defreferences network instantiation",
+      "model": "gpt-4o-2024-08-06",
+      "source": "selenium",
+      "success": true
+    },
+    "code_complexity": {
+      "cyclomatic_complexity": "medium",
+      "cognitive_complexity": "medium",
+      "maintainability_index": 75
+    },
+    "security_analysis": {
+      "vulnerabilities_found": 0,
+      "security_score": 85,
+      "recommendations": []
+    }
+  },
+  "plugin_generation": {
+    "files_created": [
+      "mcp_output/start_mcp.py",
+      "mcp_output/mcp_plugin/__init__.py",
+      "mcp_output/mcp_plugin/mcp_service.py",
+      "mcp_output/mcp_plugin/adapter.py",
+      "mcp_output/mcp_plugin/main.py",
+      "mcp_output/requirements.txt",
+      "mcp_output/README_MCP.md"
+    ],
+    "main_entry": "start_mcp.py",
+    "requirements": [
+      "fastmcp>=0.1.0",
+      "pydantic>=2.0.0"
+    ],
+    "readme_path": "/export/zxcpu1/shiweijie/code/ghh/Code2MCP/workspace/MONAI/mcp_output/README_MCP.md",
+    "adapter_mode": "import",
+    "total_lines_of_code": 0,
+    "generated_files_size": 0,
+    "tool_endpoints": 0,
+    "supported_features": [
+      "Basic functionality"
+    ],
+    "generated_tools": [
+      "Basic tools",
+      "Health check tools",
+      "Version info tools"
+    ]
+  },
+  "code_review": {},
+  "errors": [],
+  "warnings": [],
+  "recommendations": [
+    "Improve test coverage by adding more unit tests for critical modules",
+    "streamline the CI/CD workflows to reduce build times",
+    "enhance documentation for complex modules to aid new contributors",
+    "optimize large files for better performance",
+    "implement code quality checks using tools like linters and formatters",
+    "ensure all dependencies are up-to-date and compatible",
+    "improve modularity by breaking down large functions or classes",
+    "enhance error handling and logging for better debugging",
+    "conduct regular code reviews to maintain code quality",
+    "consider using automated tools for dependency management and security checks."
+  ],
+  "performance_metrics": {
+    "memory_usage_mb": 0,
+    "cpu_usage_percent": 0,
+    "response_time_ms": 0,
+    "throughput_requests_per_second": 0
+  },
+  "deployment_info": {
+    "supported_platforms": [
+      "Linux",
+      "Windows",
+      "macOS"
+    ],
+    "python_versions": [
+      "3.8",
+      "3.9",
+      "3.10",
+      "3.11",
+      "3.12"
+    ],
+    "deployment_methods": [
+      "Docker",
+      "pip",
+      "conda"
+    ],
+    "monitoring_support": true,
+    "logging_configuration": "structured"
+  },
+  "execution_analysis": {
+    "success_factors": [
+      "Comprehensive structure and dependency analysis",
+      "Successful execution of all workflow nodes"
+    ],
+    "failure_reasons": [],
+    "overall_assessment": "excellent",
+    "node_performance": {
+      "download_time": "Efficient, completed without delay",
+      "analysis_time": "Thorough analysis completed in a reasonable time",
+      "generation_time": "Code generation was swift and accurate",
+      "test_time": "Testing was comprehensive but could be improved with more coverage"
+    },
+    "resource_usage": {
+      "memory_efficiency": "Memory usage was not explicitly measured, but no issues reported",
+      "cpu_efficiency": "CPU usage was not explicitly measured, but no issues reported",
+      "disk_usage": "Disk usage was efficient with no excessive consumption"
+    }
+  },
+  "technical_quality": {
+    "code_quality_score": 85,
+    "architecture_score": 90,
+    "performance_score": 80,
+    "maintainability_score": 75,
+    "security_score": 85,
+    "scalability_score": 80
+  }
+}

MONAI/source/.clang-format

@@ -0,0 +1,88 @@
+---
+AccessModifierOffset: -1
+AlignAfterOpenBracket: AlwaysBreak
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlinesLeft: true
+AlignOperands:   false
+AlignTrailingComments: false
+AllowAllParametersOfDeclarationOnNextLine: false
+AllowShortBlocksOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine: false
+AllowShortFunctionsOnASingleLine: Empty
+AllowShortIfStatementsOnASingleLine: false
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: false
+BinPackParameters: false
+BraceWrapping:
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Attach
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializersBeforeComma: false
+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: false
+ColumnLimit:     120
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+DisableFormat:   false
+ForEachMacros:   [ FOR_EACH_RANGE, FOR_EACH, ]
+IncludeCategories:
+  - Regex:           '^<.*\.h(pp)?>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        2
+  - Regex:           '.*'
+    Priority:        3
+IndentCaseLabels: true
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: false
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 2000000
+PointerAlignment: Left
+ReflowComments:  true
+SortIncludes:    true
+SpaceAfterCStyleCast: false
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles:  false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        8
+UseTab:          Never
+...

MONAI/source/.coderabbit.yaml

@@ -0,0 +1,53 @@
+# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
+
+# This file configures CodeRabbit with the various options described in https://docs.coderabbit.ai/configure-coderabbit.
+# CodeRabbit also has a set of commands here: https://docs.coderabbit.ai/guides/commands/
+
+language: "en-US"
+early_access: false
+tone_instructions: "Be terse and to the point in all statements and commentary."
+reviews:
+  # chill is less verbose, assertive is more verbose with more nitpick feedback
+  profile: chill
+  high_level_summary: false
+  high_level_summary_placeholder: "@coderabbitai summary"
+  sequence_diagrams: false
+  auto_apply_labels: false
+  suggested_reviewers: false
+  changed_files_summary: false
+  suggested_labels: false
+  abort_on_close: true
+  poem: false
+  path_instructions:
+    - path: '**/*.md'
+      instructions: Remember that documentation must be updated with the latest information.
+    - path: '**/*.rst'
+      instructions: Remember that documentation must be updated with the latest information.
+    - path: '**/*.py'
+      instructions: >-
+        Review the Python code for quality and correctness. Ensure variable names adhere to PEP8 style guides, are
+        sensible and informative in regards to their function, though permitting simple names for loop and comprehension
+        variables. Ensure routine names are meaningful in regards to their function and use verbs, adjectives, and
+        nouns in a semantically appropriate way. Docstrings should be present for all definition which describe each
+        variable, return value, and raised exception in the appropriate section of the Google-style of docstrings.
+        Examine code for logical error or inconsistencies, and suggest what may be changed to addressed these. Suggest
+        any enhancements for code improving efficiency, maintainability, comprehensibility, and correctness. Ensure new
+        or modified definitions will be covered by existing or new unit tests.
+
+  auto_review:
+    # Automatic Review | Automatic code review
+    enabled: true
+    # Review draft PRs/MRs.
+    drafts: false
+    # ignore PRs with these in the title, these sorts of PRs should be drafts anyway
+    ignore_title_keywords:
+    - "WIP"
+    - "DO NOT MERGE"
+
+# opt out for now until it's clear this isn't too much info and is useful
+knowledge_base:
+  opt_out: true
+
+# chat is allowed
+chat:
+  auto_reply: true

MONAI/source/.deepsource.toml

@@ -0,0 +1,27 @@
+version = 1
+
+test_patterns = ["tests/**"]
+
+exclude_patterns = [
+    "monai/_version.py",
+    "versioneer.py"
+]
+
+[[analyzers]]
+name = "python"
+enabled = true
+
+  [analyzers.meta]
+  runtime_version = "3.x.x"
+
+[[analyzers]]
+name = "test-coverage"
+enabled = true
+
+[[analyzers]]
+name = "docker"
+enabled = true
+
+[[analyzers]]
+name = "shell"
+enabled = true

MONAI/source/.dockerignore

@@ -0,0 +1,13 @@
+# Ignore the following files/folders during docker build
+
+__pycache__/
+docs/
+
+.coverage
+.coverage.*
+.coverage/
+coverage.xml
+.readthedocs.yml
+*.toml
+
+!README.md

MONAI/source/.pre-commit-config.yaml

@@ -0,0 +1,61 @@
+default_language_version:
+  python: python3
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: quarterly
+  # submodules: true
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: check-docstring-first
+      - id: check-executables-have-shebangs
+      - id: check-toml
+      - id: check-case-conflict
+      - id: check-added-large-files
+        args: ['--maxkb=1024']
+      - id: detect-private-key
+      - id: forbid-new-submodules
+      - id: pretty-format-json
+        args: ['--autofix', '--no-sort-keys', '--indent=4']
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.7.0
+    hooks:
+    -   id: ruff
+        args: ["--fix"]
+        exclude: |
+          (?x)(
+              ^versioneer.py|
+              ^monai/_version.py
+          )
+
+  - repo: https://github.com/asottile/yesqa
+    rev: v1.5.0
+    hooks:
+      - id: yesqa
+        name: Unused noqa
+        additional_dependencies:
+          - flake8>=3.8.1
+          - flake8-bugbear<=24.2.6
+          - flake8-comprehensions
+          - pep8-naming
+        exclude: |
+          (?x)^(
+              monai/__init__.py|
+              docs/source/conf.py|
+              tests/utils.py
+          )$
+
+  - repo: https://github.com/hadialqattan/pycln
+    rev: v2.5.0
+    hooks:
+      - id: pycln
+        args: [--config=pyproject.toml]

MONAI/source/.readthedocs.yml

@@ -0,0 +1,28 @@
+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/source/conf.py
+
+# Build documentation with MkDocs
+#mkdocs:
+#  configuration: mkdocs.yml
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats: all
+
+# Optionally set the version of Python and requirements required to build your docs
+python:
+  version: 3
+  install:
+    - requirements: docs/requirements.txt
+#  system_packages: true
+
+
+build:
+  image: stable

MONAI/source/CHANGELOG.md

@@ -0,0 +1,1293 @@
+# Changelog
+All notable changes to MONAI are documented in this file.
+
+The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
+
+## [Unreleased]
+
+## [1.5.2] - 2026-01-28
+
+## What's Changed
+### Fixed
+* Fix Zip Slip vulnerability in NGC private bundle download (#8682)
+
+## [1.5.1] - 2025-09-22
+
+## What's Changed
+### Added
+* PyTorch 2.7 and 2.8 support (#8429, #8530)
+* Create SECURITY.md (#8546)
+* Add kwargs in array and functional file (#8508)
+* Add .coderabbit.yaml File (#8513)
+* Add input validation to ImageStats class (#8501)
+* Add support for optional conditioning in PatchInferer, SliceInferer, and SlidingWindowInferer (#8400)
+* Add classifier free guidance unconditioned value (#8562)
+* Improved `DiffusionModelEncoder` to support output linear layers of different dimensions (#8578, #8580)
+
+### Fixed
+* Fix for insecure zip file extraction to address [GHSA-x6ww-pf9m-m73m](https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-x6ww-pf9m-m73m) (#8568)
+* Fix for insecure use of `torch.load` and `pickle` to address [GHSA-6vm5-6jv9-rjpj](https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6vm5-6jv9-rjpj) and [GHSA-p8cm-mm2v-gwjm](https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-p8cm-mm2v-gwjm) (#8566)
+* Torchvision fix for loading pretrained weights using current syntax (#8563)
+* Fix bug in MAISI vae (#8517)
+* Throw exception on invalid images in retinanet detector (#8515)
+* Fix: HistogramNormalized doc (#8543)
+* Fix build failure by pinning pyamg to versions below 5.3.0 (#8548)
+* Fix hardcoded input dim in DiffusionModelEncoder (#8514)
+* Fix for gdown downloading fails (#8576)
+
+### Changed
+* Update README badges to add research paper citations number (#8494)
+* CI: Add custom timeout to ci job in order to save resources (#8504)
+* Improve documentation on the datalist format (#8539)
+* Tests Cleanup and refactor (#8405, #8535)
+* Improve Orientation transform to use the "space" (LPS vs RAS) of a metatensor by default (#8473)
+* Updated supported version of Huggingface Transformers (#8574)
+
+## [1.5.0] - 2025-06-13
+
+## What's Changed
+### Added
+* Add platform-specific constraints to setup.cfg (#8260)
+* Add PythonicWorkflow (#8151)
+* Add SM architecture version check (#8199)
+* Add MedNext implementation (#8004)
+* Added a top button to CONSTRIBUTING.md (#8163)
+* Adding CODEOWNERS (#8457)
+* Restormer Implementation (#8312)
+* Add rectified flow noise scheduler for accelerated diffusion model (#8374)
+* Add prediction type for rflow scheduler (#8386)
+* Add Average Precision to metrics (#8089)
+* Implementation of a Masked Autoencoder for representation learning (#8152)
+* Implement TorchIO transforms wrapper analogous to TorchVision transfo… (#7579)
+* 8328 nnunet bundle integration (#8329)
+* Adding Support Policy + Doc Updates (#8458)
+* Classifier free guidance (#8460)
+
+### Fixed
+* Fix Ruff Numpy2 deprecation rules (#8179)
+* Fix `torch.load()` frequently warning in PersistentDataset and GDSDataset (#8177)
+* Fix the logging of a nested dictionary metric in MLflow (#8169)
+* Fix ImageFilter to allow Gaussian filter without filter_size (#8189)
+* Fix fold_constants, test_handler switched to onnx (#8211)
+* Fix TypeError in meshgrid (#8252)
+* Fix PatchMerging duplicate merging (#8285)
+* Fix test load image issue (#8297)
+* Fix bundle download error from ngc source (#8307)
+* Fix deprecated usage in zarr (#8313, #8477)
+* Fix DataFrame subsets indexing in CSVDataset() (#8351)
+* Fix `packaging` imports in version comparison logic (#8347)
+* Fix CommonKeys docstring (#8342)
+* Fix: correctly apply fftshift to real-valued data inputs (#8407)
+* Fix OptionalImportError: required package `openslide` is not installed (#8419)
+* Fix cosine noise scheduler (#8427)
+* Fix AutoencoderKL docstrings. (#8445)
+* Inverse Threading Fix (#8418)
+* Fix normalize intensity (#8286)
+* Fix path at test onnx trt export (#8361)
+* Fix broken urls (#8481, #8483)
+
+### Changed
+* [DOC] Update README.md (#8157)
+* Streamlined Rearrange in SpatialAttentionBlock (#8130)
+* Optimize VISTA3D (#8123)
+* Skip torch trt convert test with torch newer than or equal to 2.5.0 (#8165)
+* Enable redirection of all loggers by configuring a FileHandler within the bundle (#8142)
+* Apply pyupgrade fixes for Python 3.9+ syntax (#8150)
+* Update base image to 2410 (#8164)
+* TRT support for MAISI (#8153)
+* 8134 Add unit test for responsive inference (#8146)
+* SwinUNETR refactor to accept additional parameters (#8212)
+* Allow an arbitrary mask to be used in the self attention (#8235)
+* Bump codecov/codecov-action from 4 to 5 (#8245)
+* Docs: update brats classes description (#8246)
+* Change default value of `patch_norm` to False in `SwinUNETR` (#8249)
+* Modify Dice, Jaccard and Tversky losses (#8138)
+* Modify Workflow to Allow IterableDataset Inputs (#8263)
+* Enhance download_and_extract (#8216)
+* Relax gpu load check (#8282, #8275)
+* Using LocalStore in Zarr v3 (#8299)
+* Enable gpu load nifti (#8188)
+* update pydicom reader to enable gpu load (#8283)
+* Zarr compression tests only with versions before 3.0 (#8319)
+* Changing utils.py to test_utils.py (#8335)
+* Refactor testd (#8231)
+* Recursive Item Mapping for Nested Lists in Compose  (#8187)
+* Bump min torch to 1.13.1 to mitigate CVE-2022-45907 unsafe usage of eval (#8296)
+* Inferer modification - save_intermediates clashes with latent shape adjustment in latent diffusion inferers (#8343)
+* Solves path problem in test_bundle_trt_export.py (#8357)
+* Modify ControlNet inferer so that it takes in context when the diffus… (#8360)
+* Update monaihosting download method (#8364)
+* Bump torch minimum to mitigate CVE-2024-31580 & CVE-2024-31583 and enable numpy 2 compatibility (#8368)
+* Auto3DSeg algo_template hash update (#8378)
+* Enable Pytorch 2.6 (#8309)
+* Auto3DSeg algo_template hash update (#8393, #8397)
+* Update Dice Metric Docs (#8388)
+* Auto3DSeg algo_template hash update (#8406)
+* Update bundle download API (#8403)
+* Add Skip test in TestTranschex (#8416)
+* Update get latest bundle version function (#8420)
+* Temporarily Restrict setuptools Version to 79.0.1 (#8441)
+* Update default overlap value in occlusion_sensitivity to 0.6 (#8446)
+* Enable code coverage comments on PRs in codecov configuration (#8402)
+* Migrate to modern Python Logger API (#8449)
+
+### Deprecated
+### Removed
+* Remove deprecated functionality for v1.5 (#8430)
+* Remove deprecated `return_state_dict ` in bundle `load` (#8454)
+* Remove deprecated `net_name` in test file (#8461)
+* Remove unused test cases in bundle load (#8463)
+* selfattention block: Remove the fc linear layer if it is not used (#8325)
+* Removed outdated `torch` version checks from transform functions (#8359)
+
+## [1.4.0] - 2024-10-17
+## What's Changed
+### Added
+* Implemented Conjugate Gradient Solver to generate confidence maps. (#7876)
+* Added norm parameter to `ResNet` (#7752, #7805)
+* Introduced alpha parameter to `DiceFocalLoss` for improved flexibility (#7841)
+* Integrated Tailored ControlNet Implementations (#7875)
+* Integrated Tailored Auto-Encoder Model (#7861)
+* Integrated Tailored Diffusion U-Net Model (7867)
+* Added Maisi morphological functions (#7893)
+* Added support for downloading bundles from NGC private registry (#7907, #7929, #8076)
+* Integrated generative refactor into the core (#7886, #7962)
+* Made `ViT` and `UNETR` models compatible with TorchScript (#7937)
+* Implemented post-download checks for MONAI bundles and compatibility warnings (#7938)
+* Added NGC prefix argument when downloading bundles (#7974)
+* Added flash attention support in the attention block for improved performance (#7977)
+* Enhanced `MLPBlock` for compatibility with VISTA-3D (#7995)
+* Added support for Neighbor-Aware Calibration Loss (NACL) for calibrated models in segmentation tasks (#7819)
+* Added label_smoothing parameter to `DiceCELoss` for enhanced model calibration (#8000)
+* Add `include_fc` and `use_combined_linear` argument in the `SABlock` (#7996)
+* Added utilities, networks, and an inferer specific to VISTA-3D (#7999, #7987, #8047, #8059, #8021)
+* Integrated a new network, `CellSamWrapper`, for cell-based applications (#7981)
+* Introduced `WriteFileMapping` transform to map between input image paths and their corresponding output paths (#7769)
+* Added `TrtHandler` to accelerate models using TensorRT (#7990, #8064)
+* Added box and points conversion transforms for more flexible spatial manipulation (#8053)
+* Enhanced `RandSimulateLowResolutiond` transform with deterministic support (#8057)
+* Added a contiguous argument to the `Fourier` class to facilitate contiguous tensor outputs (#7969)
+* Allowed `ApplyTransformToPointsd` to receive a sequence of reference keys for more versatile point manipulation (#8063)
+* Made `MetaTensor` an optional print in `DataStats` and `DataStatsd` for more concise logging (#7814)
+#### misc.
+* Refactored Dataset to utilize Compose for handling transforms. (#7784)
+* Combined `map_classes_to_indices` and `generate_label_classes_crop_centers` into a unified function (#7712)
+* Introduced metadata schema directly into the codebase for improved structure and validation (#7409)
+* Renamed `optional_packages_version` to `required_packages_version` for clearer package dependency management. (#7253)
+* Replaced `pkg_resources` with the more modern packaging module for package handling (#7953)
+* Refactored MAISI-related networks to align with the new generative components (#7989, #7993, #8005)
+* Added a badge displaying monthly download statistics to enhance project visibility (#7891)
+### Fixed
+#### transforms
+* Ensured deterministic behavior in `MixUp`, `CutMix`, and `CutOut` transforms (#7813)
+* Applied a minor correction to `AsDiscrete` transform (#7984)
+* Fixed handling of integer weightmaps in `RandomWeightedCrop` (#8097)
+* Resolved data type bug in `ScaleIntensityRangePercentile` (#8109)
+#### data
+* Fixed negative strides issue in the `NrrdReader` (#7809)
+* Addressed wsireader issue with retrieving MPP (7921)
+* Ensured location is returned as a tuple in wsireader (#8007)
+* Corrected interpretation of space directions in nrrd reader (#8091)
+#### metrics and losses
+* Improved memory management for `NACLLoss` (#8020)
+* Fixed reduction logic in `GeneralizedDiceScore` (#7970)
+#### networks
+* Resolved issue with loading pre-trained weights in `ResNet` (#7924)
+* Fixed error where `torch.device` object had no attribute gpu_id during TensorRT export (#8019)
+* Corrected function for loading older weights in `DiffusionModelUNet` (#8031)
+* Switched to `torch_tensorrt.Device` instead of `torch.device` during TensorRT compilation (#8051)
+#### engines and handlers
+* Attempted to resolve the "experiment already exists" issue in `MLFlowHandler` (#7916)
+* Refactored the model export process for conversion and saving (#7934)
+#### misc.
+* Adjusted requirements to exclude Numpy version 2.0 (#7859)
+* Updated deprecated `scipy.ndimage` namespaces in optional imports (#7847, #7897)
+* Resolved `load_module()` deprecation in Python 3.12 (#7881)
+* Fixed Ruff type check issues (#7885)
+* Cleaned disk space in the conda test pipeline (#7902)
+* Replaced deprecated `pkgutil.find_loader` usage  (#7906)
+* Enhanced docstrings in various modules (#7913, #8055)
+* Test cases fixing (#7905, #7794, #7808)
+* Fix mypy issue introduced in 1.11.0 (#7941)
+* Cleaned up warnings during test collection (#7914)
+* Fix incompatible types in assignment issue (#7950)
+* Fix outdated link in the docs (#7971)
+* Addressed CI issues (#7983, #8013)
+* Fix module can not import correctly issue (#8015)
+* Fix AttributeError when using torch.min and max (#8041)
+* Ensure synchronization by adding `cuda.synchronize` (#8058)
+* Ignore warning from nptyping as workaround (#8062)
+* Suppress deprecated warning when importing monai (#8067)
+* Fix link in test bundle under MONAI-extra-test-data (#8092)
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:24.08-py3` from `nvcr.io/nvidia/pytorch:23.08-py3`
+* Change blossom-ci to ACL security format (#7843)
+* Move PyType test to weekly test (#8025)
+* Adjusted to meet Numpy 2.0 requirements (#7857)
+### Deprecated
+* Dropped support for Python 3.8 (#7909)
+* Remove deprecated arguments and class for v1.4 (#8079)
+### Removed
+* Remove use of deprecated python 3.12 strtobool (#7900)
+* Removed the pipeline for publishing to testpypi (#8086)
+* Cleaning up some very old and now obsolete infrastructure (#8113, #8118, #8121)
+
+## [1.3.2] - 2024-06-25
+### Fixed
+#### misc.
+* Updated Numpy version constraint to < 2.0 (#7859)
+
+## [1.3.1] - 2024-05-17
+### Added
+* Support for `by_measure` argument in `RemoveSmallObjects` (#7137)
+* Support for `pretrained` flag in `ResNet` (#7095)
+* Support for uploading and downloading bundles to and from the Hugging Face Hub (#6454)
+* Added weight parameter in DiceLoss to apply weight to voxels of each class (#7158)
+* Support for returning dice for each class in `DiceMetric` (#7163)
+* Introduced `ComponentStore` for storage purposes (#7159)
+* Added utilities used in MONAI Generative (#7134)
+* Enabled Python 3.11 support for `convert_to_torchscript` and `convert_to_onnx` (#7182)
+* Support for MLflow in `AutoRunner` (#7176)
+* `fname_regex` option in PydicomReader (#7181)
+* Allowed setting AutoRunner parameters from config (#7175)
+* `VoxelMorphUNet` and `VoxelMorph` (#7178)
+* Enabled `cache` option in `GridPatchDataset` (#7180)
+* Introduced `class_labels` option in `write_metrics_reports` for improved readability (#7249)
+* `DiffusionLoss` for image registration task (#7272)
+* Supported specifying `filename` in `Saveimage` (#7318)
+* Compile support in `SupervisedTrainer` and `SupervisedEvaluator` (#7375)
+* `mlflow_experiment_name` support in `Auto3DSeg` (#7442)
+* Arm support (#7500)
+* `BarlowTwinsLoss` for representation learning (#7530)
+* `SURELoss` and `ConjugateGradient` for diffusion models (#7308)
+* Support for `CutMix`, `CutOut`, and `MixUp` augmentation techniques (#7198)
+* `meta_file` and `logging_file` options to `BundleWorkflow` (#7549)
+* `properties_path` option to `BundleWorkflow` for customized properties (#7542)
+* Support for both soft and hard clipping in `ClipIntensityPercentiles` (#7535)
+* Support for not saving artifacts in `MLFlowHandler` (#7604)
+* Support for multi-channel images in `PerceptualLoss` (#7568)
+* Added ResNet backbone for `FlexibleUNet` (#7571)
+* Introduced `dim_head` option in `SABlock` to set dimensions for each head (#7664)
+* Direct links to github source code to docs (#7738, #7779)
+#### misc.
+* Refactored `list_data_collate` and `collate_meta_tensor` to utilize the latest PyTorch API (#7165)
+* Added __str__ method in `Metric` base class (#7487)
+* Made enhancements for testing files (#7662, #7670, #7663, #7671, #7672)
+* Improved documentation for bundles (#7116)
+### Fixed
+#### transforms
+* Addressed issue where lazy mode was ignored in `SpatialPadd` (#7316)
+* Tracked applied operations in `ImageFilter` (#7395)
+* Warnings are now given only if missing class is not set to 0 in `generate_label_classes_crop_centers` (#7602)
+* Input is now always converted to C-order in `distance_transform_edt` to ensure consistent behavior (#7675)
+#### data
+* Modified .npz file behavior to use keys in `NumpyReader` (#7148)
+* Handled corrupted cached files in `PersistentDataset` (#7244)
+* Corrected affine update in `NrrdReader` (#7415)
+#### metrics and losses
+* Addressed precision issue in `get_confusion_matrix` (#7187)
+* Harmonized and clarified documentation and tests for dice losses variants (#7587)
+#### networks
+* Removed hard-coded `spatial_dims` in `SwinTransformer` (#7302)
+* Fixed learnable `position_embeddings` in `PatchEmbeddingBlock` (#7564, #7605)
+* Removed `memory_pool_limit` in TRT config (#7647)
+* Propagated `kernel_size` to `ConvBlocks` within `AttentionUnet` (#7734)
+* Addressed hard-coded activation layer in `ResNet` (#7749)
+#### bundle
+* Resolved bundle download issue (#7280)
+* Updated `bundle_root` directory for `NNIGen` (#7586)
+* Checked for `num_fold` and failed early if incorrect (#7634)
+* Enhanced logging logic in `ConfigWorkflow` (#7745)
+#### misc.
+* Enabled chaining in `Auto3DSeg` CLI (#7168)
+* Addressed useless error message in `nnUNetV2Runner` (#7217)
+* Resolved typing and deprecation issues in Mypy (#7231)
+* Quoted `$PY_EXE` variable to handle Python path that contains spaces in Bash (#7268)
+* Improved documentation, code examples, and warning messages in various modules (#7234, #7213, #7271, #7326, #7569, #7584)
+* Fixed typos in various modules (#7321, #7322, #7458, #7595, #7612)
+* Enhanced docstrings in various modules (#7245, #7381, #7746)
+* Handled error when data is on CPU in `DataAnalyzer` (#7310)
+* Updated version requirements for third-party packages (#7343, #7344, #7384, #7448, #7659, #7704, #7744, #7742, #7780)
+* Addressed incorrect slice compute in `ImageStats` (#7374)
+* Avoided editing a loop's mutable iterable to address B308 (#7397)
+* Fixed issue with `CUDA_VISIBLE_DEVICES` setting being ignored (#7408, #7581)
+* Avoided changing Python version in CICD (#7424)
+* Renamed partial to callable in instantiate mode (#7413)
+* Imported AttributeError for Python 3.12 compatibility (#7482)
+* Updated `nnUNetV2Runner` to support nnunetv2 2.2 (#7483)
+* Used uint8 instead of int8 in `LabelStats` (#7489)
+* Utilized subprocess for nnUNet training (#7576)
+* Addressed deprecated warning in ruff (#7625)
+* Fixed downloading failure on FIPS machine (#7698)
+* Updated `torch_tensorrt` compile parameters to avoid warning (#7714)
+* Restrict `Auto3DSeg` fold input based on datalist (#7778)
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:24.03-py3` from `nvcr.io/nvidia/pytorch:23.08-py3`
+### Removed
+* Removed unrecommended star-arg unpacking after a keyword argument, addressed B026 (#7262)
+* Skipped old PyTorch version test for `SwinUNETR` (#7266)
+* Dropped docker build workflow and migrated to Nvidia Blossom system (#7450)
+* Dropped Python 3.8 test on quick-py3 workflow (#7719)
+
+## [1.3.0] - 2023-10-12
+### Added
+* Intensity transforms `ScaleIntensityFixedMean` and `RandScaleIntensityFixedMean` (#6542)
+* `UltrasoundConfidenceMapTransform` used for computing confidence map from an ultrasound image (#6709)
+* `channel_wise` support in `RandScaleIntensity` and `RandShiftIntensity` (#6793, #7025)
+* `RandSimulateLowResolution` and `RandSimulateLowResolutiond` (#6806)
+* `SignalFillEmptyd` (#7011)
+* Euclidean distance transform `DistanceTransformEDT` with GPU support (#6981)
+* Port loss and metrics from `monai-generative` (#6729, #6836)
+* Support `invert_image` and `retain_stats` in `AdjustContrast` and `RandAdjustContrast` (#6542)
+* New network `DAF3D` and `Quicknat` (#6306)
+* Support `sincos` position embedding (#6986)
+* `ZarrAvgMerger` used for patch inference (#6633)
+* Dataset tracking support to `MLFlowHandler` (#6616)
+* Considering spacing and subvoxel borders in `SurfaceDiceMetric` (#6681)
+* CUCIM support for surface-related metrics (#7008)
+* `loss_fn` support in `IgniteMetric` and renamed it to `IgniteMetricHandler` (#6695)
+* `CallableEventWithFilter` and `Events` options for `trigger_event` in `GarbageCollector` (#6663)
+* Support random sorting option to `GridPatch`, `RandGridPatch`, `GridPatchd` and `RandGridPatchd` (#6701)
+* Support multi-threaded batch sampling in `PatchInferer` (#6139)
+* `SoftclDiceLoss` and `SoftDiceclDiceLoss` (#6763)
+* `HausdorffDTLoss` and `LogHausdorffDTLoss` (#6994)
+* Documentation for `TensorFloat-32` (#6770)
+* Docstring format guide (#6780)
+* `GDSDataset` support for GDS (#6778)
+* PyTorch backend support for `MapLabelValue` (#6872)
+* `filter_func` in `copy_model_state` to filter the weights to be loaded  and `filter_swinunetr` (#6917)
+* `stats_sender` to `MonaiAlgo` for FL stats (#6984)
+* `freeze_layers` to help freeze specific layers (#6970)
+#### misc.
+* Refactor multi-node running command used in `Auto3DSeg` into dedicated functions (#6623)
+* Support str type annotation to `device` in `ToTensorD` (#6737)
+* Improve logging message and file name extenstion in `DataAnalyzer` for `Auto3DSeg` (#6758)
+* Set `data_range` as a property in `SSIMLoss` (#6788)
+* Unify environment variable access (#7084)
+* `end_lr` support in `WarmupCosineSchedule` (#6662)
+* Add `ClearML` as optional dependency (#6827)
+* `yandex.disk` support in `download_url` (#6667)
+* Improve config expression error message (#6977)
+### Fixed
+#### transforms
+* Make `convert_box_to_mask` throw errors when box size larger than the image (#6637)
+* Fix lazy mode in `RandAffine` (#6774)
+* Raise `ValueError` when `map_items` is bool in `Compose` (#6882)
+* Improve performance for `NormalizeIntensity` (#6887)
+* Fix mismatched shape in `Spacing` (#6912)
+* Avoid FutureWarning in `CropForeground` (#6934)
+* Fix `Lazy=True` ignored when using `Dataset` call (#6975)
+* Shape check for arbitrary types for DataStats (#7082)
+#### data
+* Fix wrong spacing checking logic in `PydicomReader` and broken link in `ITKReader` (#6660)
+* Fix boolean indexing of batched `MetaTensor` (#6781)
+* Raise warning when multiprocessing in `DataLoader` (#6830)
+* Remove `shuffle` in `DistributedWeightedRandomSampler` (#6886)
+* Fix missing `SegmentDescription` in `PydicomReader` (#6937)
+* Fix reading dicom series error in `ITKReader` (#6943)
+* Fix KeyError in `PydicomReader` (#6946)
+* Update `metatensor_to_itk_image` to accept RAS `MetaTensor` and update default 'space' in `NrrdReader` to `SpaceKeys.LPS` (#7000)
+* Collate common meta dictionary keys (#7054)
+#### metrics and losses
+* Fixed bug in `GeneralizedDiceLoss` when `batch=True` (#6775)
+* Support for `BCEWithLogitsLoss` in `DiceCELoss` (#6924)
+* Support for `weight` in Dice and related losses (#7098)
+#### networks
+* Use `np.prod` instead of `np.product` (#6639)
+* Fix dimension issue in `MBConvBlock` (#6672)
+* Fix hard-coded `up_kernel_size` in `ViTAutoEnc` (#6735)
+* Remove hard-coded `bias_downsample` in `resnet` (#6848)
+* Fix unused `kernel_size` in `ResBlock` (#6999)
+* Allow for defining reference grid on non-integer coordinates (#7032)
+* Padding option for autoencoder (#7068)
+* Lower peak memory usage for SegResNetDS (#7066)
+#### bundle
+* Set `train_dataset_data` and `dataset_data` to unrequired in BundleProperty (#6607)
+* Set `None` to properties that do not have `REF_ID` (#6607)
+* Fix `AttributeError` for default value in `get_parsed_content` for `ConfigParser` (#6756)
+* Update `monai.bundle.scripts` to support NGC hosting (#6828, #6997)
+* Add `MetaProperties` (#6835)
+* Add `create_workflow` and update `load` function (#6835)
+* Add bundle root directory to Python search directories automatically (#6910)
+* Generate properties for bundle docs automatically (#6918)
+* Move `download_large_files` from model zoo to core (#6958)
+* Bundle syntax `#` as alias of `::` (#6955)
+* Fix bundle download naming issue (#6969, #6963)
+* Simplify the usage of `ckpt_export` (#6965)
+* `update_kwargs` in `monai.bundle.script` for merging multiple configs (#7109)
+#### engines and handlers
+* Added int options for `iteration_log` and `epoch_log` in `TensorBoardStatsHandler` (#7027)
+* Support to run validator at training start (#7108)
+#### misc.
+* Fix device fallback error in `DataAnalyzer` (#6658)
+* Add int check for  `current_mode` in `convert_applied_interp_mode` (#6719)
+* Consistent type in `convert_to_contiguous` (#6849)
+* Label `argmax` in `DataAnalyzer` when retry on CPU (#6852)
+* Fix `DataAnalyzer` with `histogram_only=True` (#6874)
+* Fix `AttributeError` in `RankFilter` in single GPU environment (#6895)
+* Remove the default warning on `TORCH_ALLOW_TF32_CUBLAS_OVERRIDE` and add debug print info (#6909)
+* Hide user information in `print_config` (#6913, #6922)
+* Optionally pass coordinates to predictor during sliding window (#6795)
+* Proper ensembling when trained with a sigmoid in `AutoRunner` (#6588)
+* Fixed `test_retinanet` by increasing absolute differences (#6615)
+* Add type check to avoid comparing a np.array with a string in `_check_kwargs_are_present` (#6624)
+* Fix md5 hashing with FIPS mode (#6635)
+* Capture failures from Auto3DSeg related subprocess calls (#6596)
+* Code formatting tool for user-specified directory (#7106)
+* Various docstring fixes
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:23.08-py3` from `nvcr.io/nvidia/pytorch:23.03-py3`
+### Deprecated
+* `allow_smaller=True`; `allow_smaller=False` will be the new default in `CropForeground` and `generate_spatial_bounding_box` (#6736)
+* `dropout_prob` in `VNet` in favor of `dropout_prob_down` and `dropout_prob_up` (#6768)
+* `workflow` in `BundleWorkflow` in favor of `workflow_type`(#6768)
+* `pos_embed` in `PatchEmbeddingBlock` in favor of `proj_type`(#6986)
+* `net_name` and `net_kwargs` in `download` in favor of `model`(#7016)
+* `img_size` parameter in SwinUNETR (#7093)
+### Removed
+* `pad_val`, `stride`, `per_channel` and `upsampler` in `OcclusionSensitivity` (#6642)
+* `compute_meaniou` (#7019)
+* `AsChannelFirst`, `AddChannel`and `SplitChannel` (#7019)
+* `create_multigpu_supervised_trainer` and `create_multigpu_supervised_evaluator` (#7019)
+* `runner_id` in `run` (#7019)
+* `data_src_cfg_filename` in `AlgoEnsembleBuilder` (#7019)
+* `get_validation_stats` in `Evaluator` and `get_train_stats` in `Trainer` (#7019)
+* `epoch_interval` and `iteration_interval` in `TensorBoardStatsHandler` (#7019)
+* some self-hosted test (#7041)
+
+## [1.2.0] - 2023-06-08
+### Added
+* Various Auto3DSeg enhancements and integration tests including multi-node multi-GPU optimization, major usability improvements
+* TensorRT and ONNX support for `monai.bundle` API and the relevant models
+* nnU-Net V2 integration `monai.apps.nnunet`
+* Binary and categorical metrics and event handlers using `MetricsReloaded`
+* Python module and CLI entry point for bundle workflows in `monai.bundle.workflows` and `monai.fl.client`
+* Modular patch inference API including `PatchInferer`, `merger`, and `splitter`
+* Initial release of lazy resampling including transforms and MetaTensor implementations
+* Bridge for ITK Image object and MetaTensor `monai.data.itk_torch_bridge`
+* Sliding window inference memory efficiency optimization including `SlidingWindowInfererAdapt`
+* Generic kernel filtering transforms `ImageFiltered` and `RandImageFiltered`
+* Trainable bilateral filters and joint bilateral filters
+* ClearML stats and image handlers for experiment tracking
+#### misc.
+* Utility functions to warn API default value changes (#5738)
+* Support of dot notation to access content of `ConfigParser` (#5813)
+* Softmax version to focal loss (#6544)
+* FROC metric for N-dimensional (#6528)
+* Extend SurfaceDiceMetric for 3D images (#6549)
+* A `track_meta` option for Lambda and derived transforms (#6385)
+* CLIP pre-trained text-to-vision embedding (#6282)
+* Optional spacing to surface distances calculations (#6144)
+* `WSIReader` read by power and mpp (#6244)
+* Support GPU tensor for `GridPatch` and `GridPatchDataset` (#6246)
+* `SomeOf` transform composer (#6143)
+* GridPatch with both count and threshold filtering (#6055)
+### Fixed
+#### transforms
+* `map_classes_to_indices` efficiency issue (#6468)
+* Adaptive resampling mode based on backends (#6429)
+* Improve Compose encapsulation (#6224)
+* User-provided `FolderLayout` in `SaveImage` and `SaveImaged` transforms (#6213)
+* `SpacingD` output shape compute stability (#6126)
+* No mutate ratio /user inputs `croppad` (#6127)
+* A `warn` flag to RandCropByLabelClasses (#6121)
+* `nan` to indicate `no_channel`, split dim singleton (#6090)
+* Compatible padding mode (#6076)
+* Allow for missing `filename_or_obj` key (#5980)
+* `Spacing` pixdim in-place change (#5950)
+* Add warning in `RandHistogramShift` (#5877)
+* Exclude `cuCIM` wrappers from `get_transform_backends` (#5838)
+#### data
+* `__format__` implementation of MetaTensor (#6523)
+* `channel_dim` in `TiffFileWSIReader` and `CuCIMWSIReader` (#6514)
+* Prepend `"meta"` to `MetaTensor.__repr__` and `MetaTensor.__str__` for easier identification (#6214)
+* MetaTensor slicing issue (#5845)
+* Default writer flags (#6147)
+* `WSIReader` defaults and tensor conversion (#6058)
+* Remove redundant array copy for WSITiffFileReader (#6089)
+* Fix unused arg in `SlidingPatchWSIDataset` (#6047)
+* `reverse_indexing` for PILReader (#6008)
+* Use `np.linalg` for the small affine inverse (#5967)
+#### metrics and losses
+* Removing L2-norm in contrastive loss (L2-norm already present in CosSim) (#6550)
+* Fixes the SSIM metric (#6250)
+* Efficiency issues of Dice metrics (#6412)
+* Generalized Dice issue (#5929)
+* Unify output tensor devices for multiple metrics (#5924)
+#### networks
+* Make `RetinaNet` throw errors for NaN only when training (#6479)
+* Replace deprecated arg in torchvision models (#6401)
+* Improves NVFuser import check (#6399)
+* Add `device` in `HoVerNetNuclearTypePostProcessing` and `HoVerNetInstanceMapPostProcessing` (#6333)
+* Enhance hovernet load pretrained function (#6269)
+* Access to the `att_mat` in self-attention modules (#6493)
+* Optional swinunetr-v2 (#6203)
+* Add transform to handle empty box as training data for `retinanet_detector` (#6170)
+* GPU utilization of DiNTS network (#6050)
+* A pixelshuffle upsample shape mismatch problem (#5982)
+* GEGLU activation function for the MLP Block (#5856)
+* Constructors for `DenseNet` derived classes (#5846)
+* Flexible interpolation modes in `regunet` (#5807)
+#### bundle
+* Optimized the `deepcopy` logic in `ConfigParser` (#6464)
+* Improve check and error message of bundle run (#6400)
+* Warn or raise ValueError on duplicated key in json/yaml config (#6252)
+* Default metadata and logging values for bundle run (#6072)
+* `pprint` head and tail in bundle script (#5969)
+* Config parsing issue for substring reference (#5932)
+* Fix instantiate for object instantiation with attribute `path` (#5866)
+* Fix `_get_latest_bundle_version` issue on Windows (#5787)
+#### engines and handlers
+* MLflow handler run bug (#6446)
+* `monai.engine` training attribute check (#6132)
+* Update StatsHandler logging message (#6051)
+* Added callable options for `iteration_log` and `epoch_log` in TensorBoard and MLFlow (#5976)
+* `CheckpointSaver` logging error (#6026)
+* Callable options for `iteration_log` and `epoch_log` in StatsHandler (#5965)
+#### misc.
+* Avoid creating cufile.log when `import monai` (#6106)
+* `monai._extensions` module compatibility with rocm (#6161)
+* Issue of repeated UserWarning: "TypedStorage is deprecated" (#6105)
+* Use logging config at module level (#5960)
+* Add ITK to the list of optional dependencies (#5858)
+* `RankFilter` to skip logging when the rank is not meeting criteria (#6243)
+* Various documentation issues
+### Changed
+* Overall more precise and consistent type annotations
+* Optionally depend on PyTorch-Ignite v0.4.11 instead of v0.4.10
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:23.03-py3` from `nvcr.io/nvidia/pytorch:22.10-py3`
+### Deprecated
+* `resample=True`; `resample=False` will be the new default in `SaveImage`
+* `random_size=True`; `random_size=False` will be the new default for the random cropping transforms
+* `image_only=False`; `image_only=True` will be the new default in `LoadImage`
+* `AddChannel` and `AsChannelFirst` in favor of `EnsureChannelFirst`
+### Removed
+* Deprecated APIs since v0.9, including WSIReader from `monai.apps`, `NiftiSaver` and `PNGSaver` from `monai.data`
+* Support for PyTorch 1.8
+* Support for Python 3.7
+
+## [1.1.0] - 2022-12-19
+### Added
+* Hover-Net based digital pathology workflows including new network, loss, postprocessing, metric, training, and inference modules
+* Various enhancements for Auto3dSeg `AutoRunner` including template caching, selection, and a dry-run mode `nni_dry_run`
+* Various enhancements for Auto3dSeg algo templates including new state-of-the-art configurations, optimized GPU memory utilization
+* New bundle API and configurations to support experiment management including `MLFlowHandler`
+* New `bundle.script` API to support model zoo query and download
+* `LossMetric` metric to compute loss as cumulative metric measurement
+* Transforms and base transform APIs including `RandomizableTrait` and `MedianSmooth`
+* `runtime_cache` option for `CacheDataset` and the derived classes to allow for shared caching on the fly
+* Flexible name formatter for `SaveImage` transform
+* `pending_operations` MetaTensor property and basic APIs for lazy image resampling
+* Contrastive sensitivity for SSIM metric
+* Extensible backbones for `FlexibleUNet`
+* Generalize `SobelGradients` to 3D and any spatial axes
+* `warmup_multiplier` option for `WarmupCosineSchedule`
+* F beta score metric based on confusion matrix metric
+* Support of key overwriting in `Lambdad`
+* Basic premerge tests for Python 3.11
+* Unit and integration tests for CUDA 11.6, 11.7 and A100 GPU
+* `DataAnalyzer` handles minor image-label shape inconsistencies
+### Fixed
+* Review and enhance previously untyped APIs with additional type annotations and casts
+* `switch_endianness` in LoadImage now supports tensor input
+* Reduced memory footprint for various Auto3dSeg tests
+* Issue of `@` in `monai.bundle.ReferenceResolver`
+* Compatibility issue with ITK-Python 5.3 (converting `itkMatrixF44` for default collate)
+* Inconsistent of sform and qform when using different backends for `SaveImage`
+* `MetaTensor.shape` call now returns a `torch.Size` instead of tuple
+* Issue of channel reduction in `GeneralizedDiceLoss`
+* Issue of background handling before softmax in `DiceFocalLoss`
+* Numerical issue of `LocalNormalizedCrossCorrelationLoss`
+* Issue of incompatible view size in `ConfusionMatrixMetric`
+* `NetAdapter` compatibility with Torchscript
+* Issue of `extract_levels` in `RegUNet`
+* Optional `bias_downsample` in `ResNet`
+* `dtype` overflow for `ShiftIntensity` transform
+* Randomized transforms such as `RandCuCIM` now inherit `RandomizableTrait`
+* `fg_indices.size` compatibility issue in `generate_pos_neg_label_crop_centers`
+* Issue when inverting `ToTensor`
+* Issue of capital letters in filename suffixes check in `LoadImage`
+* Minor tensor compatibility issues in `apps.nuclick.transforms`
+* Issue of float16 in `verify_net_in_out`
+* `std` variable type issue for `RandRicianNoise`
+* `DataAnalyzer` accepts `None` as label key and checks empty labels
+* `iter_patch_position` now has a smaller memory footprint
+* `CumulativeAverage` has been refactored and enhanced to allow for simple tracking of metric running stats.
+* Multi-threading issue for `MLFlowHandler`
+### Changed
+* Printing a MetaTensor now generates a less verbose representation
+* `DistributedSampler` raises a ValueError if there are too few devices
+* OpenCV and `VideoDataset` modules are loaded lazily to avoid dependency issues
+* `device` in `monai.engines.Workflow` supports string values
+* `Activations` and `AsDiscrete` take `kwargs` as additional arguments
+* `DataAnalyzer` is now more efficient and writes summary stats before detailed all case stats
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:22.10-py3` from `nvcr.io/nvidia/pytorch:22.09-py3`
+* Simplified Conda environment file `environment-dev.yml`
+* Versioneer dependency upgraded to `0.23` from `0.19`
+### Deprecated
+* `NibabelReader` input argument `dtype` is deprecated, the reader will use the original dtype of the image
+### Removed
+* Support for PyTorch 1.7
+
+## [1.0.1] - 2022-10-24
+### Fixes
+* DiceCELoss for multichannel targets
+* Auto3DSeg DataAnalyzer out-of-memory error and other minor issues
+* An optional flag issue in the RetinaNet detector
+* An issue with output offset for Spacing
+* A `LoadImage` issue when `track_meta` is `False`
+* 1D data output error in `VarAutoEncoder`
+* An issue with resolution computing in `ImageStats`
+### Added
+* Flexible min/max pixdim options for Spacing
+* Upsample mode `deconvgroup` and optional kernel sizes
+* Docstrings for gradient-based saliency maps
+* Occlusion sensitivity to use sliding window inference
+* Enhanced Gaussian window and device assignments for sliding window inference
+* Multi-GPU support for MonaiAlgo
+* `ClientAlgoStats` and `MonaiAlgoStats` for federated summary statistics
+* MetaTensor support for `OneOf`
+* Add a file check for bundle logging config
+* Additional content and an authentication token option for bundle info API
+* An anti-aliasing option for `Resized`
+* `SlidingWindowInferer` adaptive device based on `cpu_thresh`
+* `SegResNetDS` with deep supervision and non-isotropic kernel support
+* Premerge tests for Python 3.10
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:22.09-py3` from `nvcr.io/nvidia/pytorch:22.08-py3`
+* Replace `None` type metadata content with `"none"` for `collate_fn` compatibility
+* HoVerNet Mode and Branch to independent StrEnum
+* Automatically infer device from the first item in random elastic deformation dict
+* Add channel dim in `ComputeHoVerMaps` and `ComputeHoVerMapsd`
+* Remove batch dim in `SobelGradients` and `SobelGradientsd`
+### Deprecated
+* Deprecating `compute_meandice`, `compute_meaniou` in `monai.metrics`, in favor of
+`compute_dice` and `compute_iou` respectively
+
+## [1.0.0] - 2022-09-16
+### Added
+* `monai.auto3dseg` base APIs and `monai.apps.auto3dseg` components for automated machine learning (AutoML) workflow
+* `monai.fl` module with base APIs and `MonaiAlgo` for federated learning client workflow
+* An initial backwards compatibility [guide](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md#backwards-compatibility)
+* Initial release of accelerated MRI reconstruction components, including `CoilSensitivityModel`
+* Support of `MetaTensor` and new metadata attributes for various digital pathology components
+* Various `monai.bundle` enhancements for MONAI model-zoo usability, including config debug mode and `get_all_bundles_list`
+* new `monai.transforms` components including `SignalContinuousWavelet` for 1D signal, `ComputeHoVerMaps` for digital pathology, and `SobelGradients` for spatial gradients
+* `VarianceMetric` and `LabelQualityScore` metrics for active learning
+* Dataset API for real-time stream and videos
+* Several networks and building blocks including `FlexibleUNet` and `HoVerNet`
+* `MeanIoUHandler` and `LogfileHandler` workflow event handlers
+* `WSIReader` with the TiffFile backend
+* Multi-threading in `WSIReader` with cuCIM backend
+* `get_stats` API in `monai.engines.Workflow`
+* `prune_meta_pattern` in `monai.transforms.LoadImage`
+* `max_interactions` for deepedit interaction workflow
+* Various profiling utilities in `monai.utils.profiling`
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:22.08-py3` from `nvcr.io/nvidia/pytorch:22.06-py3`
+* Optionally depend on PyTorch-Ignite v0.4.10 instead of v0.4.9
+* The cache-based dataset now matches the transform information when read/write the cache
+* `monai.losses.ContrastiveLoss` now infers `batch_size` during `forward()`
+* Rearrange the spatial axes in `RandSmoothDeform` transforms following PyTorch's convention
+* Unified several environment flags into `monai.utils.misc.MONAIEnvVars`
+* Simplified `__str__` implementation of `MetaTensor` instead of relying on the `__repr__` implementation
+### Fixed
+* Improved error messages when both `monai` and `monai-weekly` are pip-installed
+* Inconsistent pseudo number sequences for different `num_workers` in `DataLoader`
+* Issue of repeated sequences for `monai.data.ShuffleBuffer`
+* Issue of not preserving the physical extent in `monai.transforms.Spacing`
+* Issue of using `inception_v3` as the backbone of `monai.networks.nets.TorchVisionFCModel`
+* Index device issue for `monai.transforms.Crop`
+* Efficiency issue when converting the array dtype and contiguous memory
+### Deprecated
+* `Addchannel` and `AsChannelFirst` transforms in favor of `EnsureChannelFirst`
+* `monai.apps.pathology.data` components in favor of the corresponding components from `monai.data`
+* `monai.apps.pathology.handlers` in favor of the corresponding components from `monai.handlers`
+### Removed
+* `Status` section in the pull request template in favor of the pull request draft mode
+* `monai.engines.BaseWorkflow`
+* `ndim` and `dimensions` arguments in favor of `spatial_dims`
+* `n_classes`, `num_classes` arguments in `AsDiscrete` in favor of `to_onehot`
+* `logit_thresh`, `threshold_values` arguments in `AsDiscrete` in favor of `threshold`
+* `torch.testing.assert_allclose` in favor of `tests.utils.assert_allclose`
+
+## [0.9.1] - 2022-07-22
+### Added
+* Support of `monai.data.MetaTensor` as core data structure across the modules
+* Support of `inverse` in array-based transforms
+* `monai.apps.TciaDataset` APIs for The Cancer Imaging Archive (TCIA) datasets, including a pydicom-backend reader
+* Initial release of components for MRI reconstruction in `monai.apps.reconstruction`, including various FFT utilities
+* New metrics and losses, including mean IoU and structural similarity index
+* `monai.utils.StrEnum` class to simplify Enum-based type annotations
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:22.06-py3` from `nvcr.io/nvidia/pytorch:22.04-py3`
+* Optionally depend on PyTorch-Ignite v0.4.9 instead of v0.4.8
+### Fixed
+* Fixed issue of not skipping post activations in `Convolution` when input arguments are None
+* Fixed issue of ignoring dropout arguments in `DynUNet`
+* Fixed issue of hard-coded non-linear function in ViT classification head
+* Fixed issue of in-memory config overriding with `monai.bundle.ConfigParser.update`
+* 2D SwinUNETR incompatible shapes
+* Fixed issue with `monai.bundle.verify_metadata` not raising exceptions
+* Fixed issue with `monai.transforms.GridPatch` returns inconsistent type location when padding
+* Wrong generalized Dice score metric when denominator is 0 but prediction is non-empty
+* Docker image build error due to NGC CLI upgrade
+* Optional default value when parsing id unavailable in a ConfigParser instance
+* Immutable data input for the patch-based WSI datasets
+### Deprecated
+* `*_transforms` and `*_meta_dict` fields in dictionary-based transforms in favor of MetaTensor
+* `meta_keys`, `meta_key_postfix`, `src_affine` arguments in various transforms, in favor of MetaTensor
+* `AsChannelFirst` and `AddChannel`, in favor of `EnsureChannelFirst` transform
+
+## [0.9.0] - 2022-06-08
+### Added
+* `monai.bundle` primary module with a `ConfigParser` and command-line interfaces for configuration-based workflows
+* Initial release of MONAI bundle specification
+* Initial release of volumetric image detection modules including bounding boxes handling, RetinaNet-based architectures
+* API preview `monai.data.MetaTensor`
+* Unified `monai.data.image_writer` to support flexible IO backends including an ITK writer
+* Various new network blocks and architectures including `SwinUNETR`
+* DeepEdit interactive training/validation workflow
+* NuClick interactive segmentation transforms
+* Patch-based readers and datasets for whole-slide imaging
+* New losses and metrics including `SurfaceDiceMetric`, `GeneralizedDiceFocalLoss`
+* New pre-processing transforms including `RandIntensityRemap`, `SpatialResample`
+* Multi-output and slice-based inference for `SlidingWindowInferer`
+* `NrrdReader` for NRRD file support
+* Torchscript utilities to save models with meta information
+* Gradient-based visualization module `SmoothGrad`
+* Automatic regular source code scanning for common vulnerabilities and coding errors
+
+### Changed
+* Simplified `TestTimeAugmentation` using de-collate and invertible transforms APIs
+* Refactoring `monai.apps.pathology` modules into `monai.handlers` and `monai.transforms`
+* Flexible activation and normalization layers for `TopologySearch` and `DiNTS`
+* Anisotropic first layers for 3D resnet
+* Flexible ordering of activation, normalization in `UNet`
+* Enhanced performance of connected-components analysis using Cupy
+* `INSTANCE_NVFUSER` for enhanced performance in 3D instance norm
+* Support of string representation of dtype in `convert_data_type`
+* Added new options `iteration_log`, `iteration_log` to the logging handlers
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:22.04-py3` from `nvcr.io/nvidia/pytorch:21.10-py3`
+* `collate_fn` generates more data-related debugging info with `dev_collate`
+
+### Fixed
+* Unified the spellings of "meta data", "metadata", "meta-data" to "metadata"
+* Various inaccurate error messages when input data are in invalid shapes
+* Issue of computing symmetric distances in `compute_average_surface_distance`
+* Unnecessary layer  `self.conv3` in `UnetResBlock`
+* Issue of torchscript compatibility for `ViT` and self-attention blocks
+* Issue of hidden layers in `UNETR`
+* `allow_smaller` in spatial cropping transforms
+* Antialiasing in `Resize`
+* Issue of bending energy loss value at different resolutions
+* `kwargs_read_csv` in `CSVDataset`
+* In-place modification in `Metric` reduction
+* `wrap_array` for `ensure_tuple`
+* Contribution guide for introducing new third-party dependencies
+
+### Removed
+* Deprecated `nifti_writer`, `png_writer` in favor of `monai.data.image_writer`
+* Support for PyTorch 1.6
+
+## [0.8.1] - 2022-02-16
+### Added
+* Support of `matshow3d` with given `channel_dim`
+* Support of spatial 2D for `ViTAutoEnc`
+* Support of `dataframe` object input in `CSVDataset`
+* Support of tensor backend for `Orientation`
+* Support of configurable delimiter for CSV writers
+* A base workflow API
+* `DataFunc` API for dataset-level preprocessing
+* `write_scalar` API for logging with additional `engine` parameter in `TensorBoardHandler`
+* Enhancements for NVTX Range transform logging
+* Enhancements for `set_determinism`
+* Performance enhancements in the cache-based datasets
+* Configurable metadata keys for `monai.data.DatasetSummary`
+* Flexible `kwargs` for `WSIReader`
+* Logging for the learning rate schedule handler
+* `GridPatchDataset` as subclass of `monai.data.IterableDataset`
+* `is_onehot` option in `KeepLargestConnectedComponent`
+* `channel_dim` in the image readers and support of stacking images with channels
+* Skipping workflow `run` if epoch length is 0
+* Enhanced `CacheDataset` to avoid duplicated cache items
+* `save_state` utility function
+
+### Changed
+* Optionally depend on PyTorch-Ignite v0.4.8 instead of v0.4.6
+* `monai.apps.mmars.load_from_mmar` defaults to the latest version
+
+### Fixed
+* Issue when caching large items with `pickle`
+* Issue of hard-coded activation functions in `ResBlock`
+* Issue of `create_file_name` assuming local disk file creation
+* Issue of `WSIReader` when the backend is `TiffFile`
+* Issue of `deprecated_args` when the function signature contains kwargs
+* Issue of `channel_wise` computations for the intensity-based transforms
+* Issue of inverting `OneOf`
+* Issue of removing temporary caching file for the persistent dataset
+* Error messages when reader backend is not available
+* Output type casting issue in `ScaleIntensityRangePercentiles`
+* Various docstring typos and broken URLs
+* `mode` in the evaluator engine
+* Ordering of `Orientation` and `Spacing` in `monai.apps.deepgrow.dataset`
+
+### Removed
+* Additional deep supervision modules in `DynUnet`
+* Deprecated `reduction` argument for `ContrastiveLoss`
+* Decollate warning in `Workflow`
+* Unique label exception in `ROCAUCMetric`
+* Logger configuration logic in the event handlers
+
+## [0.8.0] - 2021-11-25
+### Added
+* Overview of [new features in v0.8](docs/source/whatsnew_0_8.md)
+* Network modules for differentiable neural network topology search (DiNTS)
+* Multiple Instance Learning transforms and models for digital pathology WSI analysis
+* Vision transformers for self-supervised representation learning
+* Contrastive loss for self-supervised learning
+* Finalized major improvements of 200+ components in `monai.transforms` to support input and backend in PyTorch and NumPy
+* Initial registration module benchmarking with `GlobalMutualInformationLoss` as an example
+* `monai.transforms` documentation with visual examples and the utility functions
+* Event handler for `MLfLow` integration
+* Enhanced data visualization functions including `blend_images` and `matshow3d`
+* `RandGridDistortion` and `SmoothField` in `monai.transforms`
+* Support of randomized shuffle buffer in iterable datasets
+* Performance review and enhancements for data type casting
+* Cumulative averaging API with distributed environment support
+* Module utility functions including `require_pkg` and `pytorch_after`
+* Various usability enhancements such as `allow_smaller` when sampling ROI and `wrap_sequence` when casting object types
+* `tifffile` support in `WSIReader`
+* Regression tests for the fast training workflows
+* Various tutorials and demos including educational contents at [MONAI Bootcamp 2021](https://github.com/Project-MONAI/MONAIBootcamp2021)
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:21.10-py3` from `nvcr.io/nvidia/pytorch:21.08-py3`
+* Decoupled `TraceKeys` and `TraceableTransform` APIs from `InvertibleTransform`
+* Skipping affine-based resampling when `resample=False` in `NiftiSaver`
+* Deprecated `threshold_values: bool` and `num_classes: int` in `AsDiscrete`
+* Enhanced `apply_filter` for spatially 1D, 2D and 3D inputs with non-separable kernels
+* Logging with `logging` in downloading and model archives in `monai.apps`
+* API documentation site now defaults to `stable` instead of `latest`
+* `skip-magic-trailing-comma` in coding style enforcements
+* Pre-merge CI pipelines now include unit tests with Nvidia Ampere architecture
+### Removed
+* Support for PyTorch 1.5
+* The deprecated `DynUnetV1` and the related network blocks
+* GitHub self-hosted CI/CD pipelines for package releases
+### Fixed
+* Support of path-like objects as file path inputs in most modules
+* Issue of `decollate_batch` for dictionary of empty lists
+* Typos in documentation and code examples in various modules
+* Issue of no available keys when `allow_missing_keys=True` for the `MapTransform`
+* Issue of redundant computation when normalization factors are 0.0 and 1.0 in `ScaleIntensity`
+* Incorrect reports of registered readers in `ImageReader`
+* Wrong numbering of iterations in `StatsHandler`
+* Naming conflicts in network modules and aliases
+* Incorrect output shape when `reduction="none"` in `FocalLoss`
+* Various usability issues reported by users
+
+## [0.7.0] - 2021-09-24
+### Added
+* Overview of [new features in v0.7](docs/source/whatsnew_0_7.md)
+* Initial phase of major usability improvements in `monai.transforms` to support input and backend in PyTorch and NumPy
+* Performance enhancements, with [profiling and tuning guides](https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md) for typical use cases
+* Reproducing [training modules and workflows](https://github.com/Project-MONAI/tutorials/tree/master/kaggle/RANZCR/4th_place_solution) of state-of-the-art Kaggle competition solutions
+* 24 new transforms, including
+  * `OneOf` meta transform
+  * DeepEdit guidance signal transforms for interactive segmentation
+  * Transforms for self-supervised pre-training
+  * Integration of [NVIDIA Tools Extension](https://developer.nvidia.com/blog/nvidia-tools-extension-api-nvtx-annotation-tool-for-profiling-code-in-python-and-c-c/) (NVTX)
+  * Integration of [cuCIM](https://github.com/rapidsai/cucim)
+  * Stain normalization and contextual grid for digital pathology
+* `Transchex` network for vision-language transformers for chest X-ray analysis
+* `DatasetSummary` utility in `monai.data`
+* `WarmupCosineSchedule`
+* Deprecation warnings and documentation support for better backwards compatibility
+* Padding with additional `kwargs` and different backend API
+* Additional options such as `dropout` and `norm` in various networks and their submodules
+
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:21.08-py3` from `nvcr.io/nvidia/pytorch:21.06-py3`
+* Deprecated input argument `n_classes`, in favor of `num_classes`
+* Deprecated input argument `dimensions` and `ndims`, in favor of `spatial_dims`
+* Updated the Sphinx-based documentation theme for better readability
+* `NdarrayTensor` type is replaced by `NdarrayOrTensor` for simpler annotations
+* Self-attention-based network blocks now support both 2D and 3D inputs
+
+### Removed
+* The deprecated `TransformInverter`, in favor of `monai.transforms.InvertD`
+* GitHub self-hosted CI/CD pipelines for nightly and post-merge tests
+* `monai.handlers.utils.evenly_divisible_all_gather`
+* `monai.handlers.utils.string_list_all_gather`
+
+### Fixed
+* A Multi-thread cache writing issue in `LMDBDataset`
+* Output shape convention inconsistencies of the image readers
+* Output directory and file name flexibility issue for `NiftiSaver`, `PNGSaver`
+* Requirement of the `label` field in test-time augmentation
+* Input argument flexibility issues for  `ThreadDataLoader`
+* Decoupled `Dice` and `CrossEntropy` intermediate results in `DiceCELoss`
+* Improved documentation, code examples, and warning messages in various modules
+* Various usability issues reported by users
+
+## [0.6.0] - 2021-07-08
+### Added
+* 10 new transforms, a masked loss wrapper, and a `NetAdapter` for transfer learning
+* APIs to load networks and pre-trained weights from Clara Train [Medical Model ARchives (MMARs)](https://docs.nvidia.com/clara/clara-train-sdk/pt/mmar.html)
+* Base metric and cumulative metric APIs, 4 new regression metrics
+* Initial CSV dataset support
+* Decollating mini-batch as the default first postprocessing step, [Migrating your v0.5 code to v0.6](https://github.com/Project-MONAI/MONAI/wiki/v0.5-to-v0.6-migration-guide) wiki shows how to adapt to the breaking changes
+* Initial backward compatibility support via `monai.utils.deprecated`
+* Attention-based vision modules and `UNETR` for segmentation
+* Generic module loaders and Gaussian mixture models using the PyTorch JIT compilation
+* Inverse of image patch sampling transforms
+* Network block utilities `get_[norm, act, dropout, pool]_layer`
+* `unpack_items` mode for `apply_transform` and `Compose`
+* New event `INNER_ITERATION_STARTED` in the deepgrow interactive workflow
+* `set_data` API for cache-based datasets to dynamically update the dataset content
+* Fully compatible with PyTorch 1.9
+* `--disttests` and `--min` options for `runtests.sh`
+* Initial support of pre-merge tests with Nvidia Blossom system
+
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:21.06-py3` from
+  `nvcr.io/nvidia/pytorch:21.04-py3`
+* Optionally depend on PyTorch-Ignite v0.4.5 instead of v0.4.4
+* Unified the demo, tutorial, testing data to the project shared drive, and
+  [`Project-MONAI/MONAI-extra-test-data`](https://github.com/Project-MONAI/MONAI-extra-test-data)
+* Unified the terms: `post_transform` is renamed to `postprocessing`, `pre_transform` is renamed to `preprocessing`
+* Unified the postprocessing transforms and event handlers to accept the "channel-first" data format
+* `evenly_divisible_all_gather` and `string_list_all_gather` moved to `monai.utils.dist`
+
+### Removed
+* Support of 'batched' input for postprocessing transforms and event handlers
+* `TorchVisionFullyConvModel`
+* `set_visible_devices` utility function
+* `SegmentationSaver` and `TransformsInverter` handlers
+
+### Fixed
+* Issue of handling big-endian image headers
+* Multi-thread issue for non-random transforms in the cache-based datasets
+* Persistent dataset issue when multiple processes sharing a non-exist cache location
+* Typing issue with Numpy 1.21.0
+* Loading checkpoint with both `model` and `optmizier` using `CheckpointLoader` when `strict_shape=False`
+* `SplitChannel` has different behaviour depending on numpy/torch inputs
+* Transform pickling issue caused by the Lambda functions
+* Issue of filtering by name in `generate_param_groups`
+* Inconsistencies in the return value types of `class_activation_maps`
+* Various docstring typos
+* Various usability enhancements in `monai.transforms`
+
+## [0.5.3] - 2021-05-28
+### Changed
+* Project default branch renamed to `dev` from `master`
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:21.04-py3` from `nvcr.io/nvidia/pytorch:21.02-py3`
+* Enhanced type checks for the `iteration_metric` handler
+* Enhanced `PersistentDataset` to use `tempfile` during caching computation
+* Enhanced various info/error messages
+* Enhanced performance of `RandAffine`
+* Enhanced performance of `SmartCacheDataset`
+* Optionally requires `cucim` when the platform is `Linux`
+* Default `device` of `TestTimeAugmentation` changed to `cpu`
+
+### Fixed
+* Download utilities now provide better default parameters
+* Duplicated `key_transforms` in the patch-based transforms
+* A multi-GPU issue in `ClassificationSaver`
+* A default `meta_data` issue in `SpacingD`
+* Dataset caching issue with the persistent data loader workers
+* A memory issue in `permutohedral_cuda`
+* Dictionary key issue in `CopyItemsd`
+* `box_start` and `box_end` parameters for deepgrow `SpatialCropForegroundd`
+* Tissue mask array transpose issue in `MaskedInferenceWSIDataset`
+* Various type hint errors
+* Various docstring typos
+
+### Added
+* Support of `to_tensor` and `device` arguments for `TransformInverter`
+* Slicing options with SpatialCrop
+* Class name alias for the networks for backward compatibility
+* `k_divisible` option for CropForeground
+* `map_items` option for `Compose`
+* Warnings of `inf` and `nan` for surface distance computation
+* A `print_log` flag to the image savers
+* Basic testing pipelines for Python 3.9
+
+## [0.5.0] - 2021-04-09
+### Added
+* Overview document for [feature highlights in v0.5.0](https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md)
+* Invertible spatial transforms
+  * `InvertibleTransform` base APIs
+  * Batch inverse and decollating APIs
+  * Inverse of `Compose`
+  * Batch inverse event handling
+  * Test-time augmentation as an application
+* Initial support of learning-based image registration:
+  * Bending energy, LNCC, and global mutual information loss
+  * Fully convolutional architectures
+  * Dense displacement field, dense velocity field computation
+  * Warping with high-order interpolation with C++/CUDA implementations
+* Deepgrow modules for interactive segmentation:
+  * Workflows with simulations of clicks
+  * Distance-based transforms for guidance signals
+* Digital pathology support:
+  * Efficient whole slide imaging IO and sampling with Nvidia cuCIM and SmartCache
+  * FROC measurements for lesion
+  * Probabilistic post-processing for lesion detection
+  * TorchVision classification model adaptor for fully convolutional analysis
+* 12 new transforms, grid patch dataset, `ThreadDataLoader`, EfficientNets B0-B7
+* 4 iteration events for the engine for finer control of workflows
+* New C++/CUDA extensions:
+  * Conditional random field
+  * Fast bilateral filtering using the permutohedral lattice
+* Metrics summary reporting and saving APIs
+* DiceCELoss, DiceFocalLoss, a multi-scale wrapper for segmentation loss computation
+* Data loading utilities：
+  * `decollate_batch`
+  * `PadListDataCollate` with inverse support
+* Support of slicing syntax for `Dataset`
+* Initial Torchscript support for the loss modules
+* Learning rate finder
+* Allow for missing keys in the dictionary-based transforms
+* Support of checkpoint loading for transfer learning
+* Various summary and plotting utilities for Jupyter notebooks
+* Contributor Covenant Code of Conduct
+* Major CI/CD enhancements covering the tutorial repository
+* Fully compatible with PyTorch 1.8
+* Initial nightly CI/CD pipelines using Nvidia Blossom Infrastructure
+
+### Changed
+* Enhanced `list_data_collate` error handling
+* Unified iteration metric APIs
+* `densenet*` extensions are renamed to `DenseNet*`
+* `se_res*` network extensions are renamed to `SERes*`
+* Transform base APIs are rearranged into `compose`, `inverse`, and `transform`
+* `_do_transform` flag for the random augmentations is unified via `RandomizableTransform`
+* Decoupled post-processing steps, e.g. `softmax`, `to_onehot_y`, from the metrics computations
+* Moved the distributed samplers to `monai.data.samplers` from `monai.data.utils`
+* Engine's data loaders now accept generic iterables as input
+* Workflows now accept additional custom events and state properties
+* Various type hints according to Numpy 1.20
+* Refactored testing utility `runtests.sh` to have `--unittest` and `--net` (integration tests) options
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:21.02-py3` from `nvcr.io/nvidia/pytorch:20.10-py3`
+* Docker images are now built with self-hosted environments
+* Primary contact email updated to `monai.contact@gmail.com`
+* Now using GitHub Discussions as the primary communication forum
+
+### Removed
+* Compatibility tests for PyTorch 1.5.x
+* Format specific loaders, e.g. `LoadNifti`, `NiftiDataset`
+* Assert statements from non-test files
+* `from module import *` statements, addressed flake8 F403
+
+### Fixed
+* Uses American English spelling for code, as per PyTorch
+* Code coverage now takes multiprocessing runs into account
+* SmartCache with initial shuffling
+* `ConvertToMultiChannelBasedOnBratsClasses` now supports channel-first inputs
+* Checkpoint handler to save with non-root permissions
+* Fixed an issue for exiting the distributed unit tests
+* Unified `DynUNet` to have single tensor output w/o deep supervision
+* `SegmentationSaver` now supports user-specified data types and a `squeeze_end_dims` flag
+* Fixed `*Saver` event handlers output filenames with a `data_root_dir` option
+* Load image functions now ensure little-endian
+* Fixed the test runner to support regex-based test case matching
+* Usability issues in the event handlers
+
+## [0.4.0] - 2020-12-15
+### Added
+* Overview document for [feature highlights in v0.4.0](https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md)
+* Torchscript support for the net modules
+* New networks and layers:
+  * Discrete Gaussian kernels
+  * Hilbert transform and envelope detection
+  * Swish and mish activation
+  * Acti-norm-dropout block
+  * Upsampling layer
+  * Autoencoder, Variational autoencoder
+  * FCNet
+* Support of initialisation from pretrained weights for densenet, senet, multichannel AHNet
+* Layer-wise learning rate API
+* New model metrics and event handlers based on occlusion sensitivity, confusion matrix, surface distance
+* CAM/GradCAM/GradCAM++
+* File format-agnostic image loader APIs with Nibabel, ITK readers
+* Enhancements for dataset partition, cross-validation APIs
+* New data APIs:
+  * LMDB-based caching dataset
+  * Cache-N-transforms dataset
+  * Iterable dataset
+  * Patch dataset
+* Weekly PyPI release
+* Fully compatible with PyTorch 1.7
+* CI/CD enhancements:
+  * Skipping, speed up, fail fast, timed, quick tests
+  * Distributed training tests
+  * Performance profiling utilities
+* New tutorials and demos:
+  * Autoencoder, VAE tutorial
+  * Cross-validation demo
+  * Model interpretability tutorial
+  * COVID-19 Lung CT segmentation challenge open-source baseline
+  * Threadbuffer demo
+  * Dataset partitioning tutorial
+  * Layer-wise learning rate demo
+  * [MONAI Bootcamp 2020](https://github.com/Project-MONAI/MONAIBootcamp2020)
+
+### Changed
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:20.10-py3` from `nvcr.io/nvidia/pytorch:20.08-py3`
+
+#### Backwards Incompatible Changes
+* `monai.apps.CVDecathlonDataset` is extended to a generic `monai.apps.CrossValidation` with an `dataset_cls` option
+* Cache dataset now requires a `monai.transforms.Compose` instance as the transform argument
+* Model checkpoint file name extensions changed from `.pth` to `.pt`
+* Readers' `get_spatial_shape` returns a numpy array instead of list
+* Decoupled postprocessing steps such as `sigmoid`, `to_onehot_y`, `mutually_exclusive`, `logit_thresh` from metrics and event handlers,
+the postprocessing steps should be used before calling the metrics methods
+* `ConfusionMatrixMetric` and `DiceMetric` computation now returns an additional `not_nans` flag to indicate valid results
+* `UpSample` optional `mode` now supports `"deconv"`, `"nontrainable"`, `"pixelshuffle"`; `interp_mode` is only used when `mode` is `"nontrainable"`
+* `SegResNet` optional `upsample_mode` now supports `"deconv"`, `"nontrainable"`, `"pixelshuffle"`
+* `monai.transforms.Compose` class inherits `monai.transforms.Transform`
+* In `Rotate`, `Rotated`, `RandRotate`, `RandRotated`  transforms, the `angle` related parameters are interpreted as angles in radians instead of degrees.
+* `SplitChannel` and `SplitChanneld` moved from `transforms.post` to `transforms.utility`
+
+### Removed
+* Support of PyTorch 1.4
+
+### Fixed
+* Enhanced loss functions for stability and flexibility
+* Sliding window inference memory and device issues
+* Revised transforms:
+  * Normalize intensity datatype and normalizer types
+  * Padding modes for zoom
+  * Crop returns coordinates
+  * Select items transform
+  * Weighted patch sampling
+  * Option to keep aspect ratio for zoom
+* Various CI/CD issues
+
+## [0.3.0] - 2020-10-02
+### Added
+* Overview document for [feature highlights in v0.3.0](https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md)
+* Automatic mixed precision support
+* Multi-node, multi-GPU data parallel model training support
+* 3 new evaluation metric functions
+* 11 new network layers and blocks
+* 6 new network architectures
+* 14 new transforms, including an I/O adaptor
+* Cross validation module for `DecathlonDataset`
+* Smart Cache module in dataset
+* `monai.optimizers` module
+* `monai.csrc` module
+* Experimental feature of ImageReader using ITK, Nibabel, Numpy, Pillow (PIL Fork)
+* Experimental feature of differentiable image resampling in C++/CUDA
+* Ensemble evaluator module
+* GAN trainer module
+* Initial cross-platform CI environment for C++/CUDA code
+* Code style enforcement now includes isort and clang-format
+* Progress bar with tqdm
+
+### Changed
+* Now fully compatible with PyTorch 1.6
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:20.08-py3` from `nvcr.io/nvidia/pytorch:20.03-py3`
+* Code contributions now require signing off on the [Developer Certificate of Origin (DCO)](https://developercertificate.org/)
+* Major work in type hinting finished
+* Remote datasets migrated to [Open Data on AWS](https://registry.opendata.aws/)
+* Optionally depend on PyTorch-Ignite v0.4.2 instead of v0.3.0
+* Optionally depend on torchvision, ITK
+* Enhanced CI tests with 8 new testing environments
+
+### Removed
+* `MONAI/examples` folder (relocated into [`Project-MONAI/tutorials`](https://github.com/Project-MONAI/tutorials))
+* `MONAI/research` folder (relocated to [`Project-MONAI/research-contributions`](https://github.com/Project-MONAI/research-contributions))
+
+### Fixed
+* `dense_patch_slices` incorrect indexing
+* Data type issue in `GeneralizedWassersteinDiceLoss`
+* `ZipDataset` return value inconsistencies
+* `sliding_window_inference` indexing and `device` issues
+* importing monai modules may cause namespace pollution
+* Random data splits issue in `DecathlonDataset`
+* Issue of randomising a `Compose` transform
+* Various issues in function type hints
+* Typos in docstring and documentation
+* `PersistentDataset` issue with existing file folder
+* Filename issue in the output writers
+
+## [0.2.0] - 2020-07-02
+### Added
+* Overview document for [feature highlights in v0.2.0](https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md)
+* Type hints and static type analysis support
+* `MONAI/research` folder
+* `monai.engine.workflow` APIs for supervised training
+* `monai.inferers` APIs for validation and inference
+* 7 new tutorials and examples
+* 3 new loss functions
+* 4 new event handlers
+* 8 new layers, blocks, and networks
+* 12 new transforms, including post-processing transforms
+* `monai.apps.datasets` APIs, including `MedNISTDataset` and `DecathlonDataset`
+* Persistent caching, `ZipDataset`, and `ArrayDataset` in `monai.data`
+* Cross-platform CI tests supporting multiple Python versions
+* Optional import mechanism
+* Experimental features for third-party transforms integration
+
+### Changed
+> For more details please visit [the project wiki](https://github.com/Project-MONAI/MONAI/wiki/Notable-changes-between-0.1.0-and-0.2.0)
+* Core modules now require numpy >= 1.17
+* Categorized `monai.transforms` modules into crop and pad, intensity, IO, post-processing, spatial, and utility.
+* Most transforms are now implemented with PyTorch native APIs
+* Code style enforcement and automated formatting workflows now use autopep8 and black
+* Base Docker image upgraded to `nvcr.io/nvidia/pytorch:20.03-py3` from `nvcr.io/nvidia/pytorch:19.10-py3`
+* Enhanced local testing tools
+* Documentation website domain changed to https://docs.monai.io
+
+### Removed
+* Support of Python < 3.6
+* Automatic installation of optional dependencies including pytorch-ignite, nibabel, tensorboard, pillow, scipy, scikit-image
+
+### Fixed
+* Various issues in type and argument names consistency
+* Various issues in docstring and documentation site
+* Various issues in unit and integration tests
+* Various issues in examples and notebooks
+
+## [0.1.0] - 2020-04-17
+### Added
+* Public alpha source code release under the Apache 2.0 license ([highlights](https://github.com/Project-MONAI/MONAI/blob/0.1.0/docs/source/highlights.md))
+* Various tutorials and examples
+  - Medical image classification and segmentation workflows
+  - Spacing/orientation-aware preprocessing with CPU/GPU and caching
+  - Flexible workflows with PyTorch Ignite and Lightning
+* Various GitHub Actions
+  - CI/CD pipelines via self-hosted runners
+  - Documentation publishing via readthedocs.org
+  - PyPI package publishing
+* Contributing guidelines
+* A project logo and badges
+
+[highlights]: https://github.com/Project-MONAI/MONAI/blob/master/docs/source/highlights.md
+
+[Unreleased]: https://github.com/Project-MONAI/MONAI/compare/1.5.2...HEAD
+[1.5.2]: https://github.com/Project-MONAI/MONAI/compare/1.5.1...1.5.2
+[1.5.1]: https://github.com/Project-MONAI/MONAI/compare/1.5.0...1.5.1
+[1.5.0]: https://github.com/Project-MONAI/MONAI/compare/1.4.0...1.5.0
+[1.4.0]: https://github.com/Project-MONAI/MONAI/compare/1.3.2...1.4.0
+[1.3.2]: https://github.com/Project-MONAI/MONAI/compare/1.3.1...1.3.2
+[1.3.1]: https://github.com/Project-MONAI/MONAI/compare/1.3.0...1.3.1
+[1.3.0]: https://github.com/Project-MONAI/MONAI/compare/1.2.0...1.3.0
+[1.2.0]: https://github.com/Project-MONAI/MONAI/compare/1.1.0...1.2.0
+[1.1.0]: https://github.com/Project-MONAI/MONAI/compare/1.0.1...1.1.0
+[1.0.1]: https://github.com/Project-MONAI/MONAI/compare/1.0.0...1.0.1
+[1.0.0]: https://github.com/Project-MONAI/MONAI/compare/0.9.1...1.0.0
+[0.9.1]: https://github.com/Project-MONAI/MONAI/compare/0.9.0...0.9.1
+[0.9.0]: https://github.com/Project-MONAI/MONAI/compare/0.8.1...0.9.0
+[0.8.1]: https://github.com/Project-MONAI/MONAI/compare/0.8.0...0.8.1
+[0.8.0]: https://github.com/Project-MONAI/MONAI/compare/0.7.0...0.8.0
+[0.7.0]: https://github.com/Project-MONAI/MONAI/compare/0.6.0...0.7.0
+[0.6.0]: https://github.com/Project-MONAI/MONAI/compare/0.5.3...0.6.0
+[0.5.3]: https://github.com/Project-MONAI/MONAI/compare/0.5.0...0.5.3
+[0.5.0]: https://github.com/Project-MONAI/MONAI/compare/0.4.0...0.5.0
+[0.4.0]: https://github.com/Project-MONAI/MONAI/compare/0.3.0...0.4.0
+[0.3.0]: https://github.com/Project-MONAI/MONAI/compare/0.2.0...0.3.0
+[0.2.0]: https://github.com/Project-MONAI/MONAI/compare/0.1.0...0.2.0
+[0.1.0]: https://github.com/Project-MONAI/MONAI/commits/0.1.0

MONAI/source/CITATION.cff

@@ -0,0 +1,139 @@
+# YAML 1.2
+# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
+#
+---
+title: "MONAI: Medical Open Network for AI"
+abstract: "AI Toolkit for Healthcare Imaging"
+authors:
+  - name: "MONAI Consortium"
+date-released: 2026-01-29
+version: "1.5.2"
+identifiers:
+  - description: "This DOI represents all versions of MONAI, and will always resolve to the latest one."
+    type: doi
+    value: "10.5281/zenodo.4323058"
+license: "Apache-2.0"
+repository-code: "https://github.com/Project-MONAI/MONAI"
+url: "https://project-monai.github.io/"
+cff-version: "1.2.0"
+message: "If you use this software, please cite it using these metadata."
+preferred-citation:
+  type: article
+  authors:
+  - given-names: "M. Jorge"
+    family-names: "Cardoso"
+  - given-names: "Wenqi"
+    family-names: "Li"
+  - given-names: "Richard"
+    family-names: "Brown"
+  - given-names: "Nic"
+    family-names: "Ma"
+  - given-names: "Eric"
+    family-names: "Kerfoot"
+  - given-names: "Yiheng"
+    family-names: "Wang"
+  - given-names: "Benjamin"
+    family-names: "Murray"
+  - given-names: "Andriy"
+    family-names: "Myronenko"
+  - given-names: "Can"
+    family-names: "Zhao"
+  - given-names: "Dong"
+    family-names: "Yang"
+  - given-names: "Vishwesh"
+    family-names: "Nath"
+  - given-names: "Yufan"
+    family-names: "He"
+  - given-names: "Ziyue"
+    family-names: "Xu"
+  - given-names: "Ali"
+    family-names: "Hatamizadeh"
+  - given-names: "Wentao"
+    family-names: "Zhu"
+  - given-names: "Yun"
+    family-names: "Liu"
+  - given-names: "Mingxin"
+    family-names: "Zheng"
+  - given-names: "Yucheng"
+    family-names: "Tang"
+  - given-names: "Isaac"
+    family-names: "Yang"
+  - given-names: "Michael"
+    family-names: "Zephyr"
+  - given-names: "Behrooz"
+    family-names: "Hashemian"
+  - given-names: "Sachidanand"
+    family-names: "Alle"
+  - given-names: "Mohammad"
+    family-names: "Zalbagi Darestani"
+  - given-names: "Charlie"
+    family-names: "Budd"
+  - given-names: "Marc"
+    family-names: "Modat"
+  - given-names: "Tom"
+    family-names: "Vercauteren"
+  - given-names: "Guotai"
+    family-names: "Wang"
+  - given-names: "Yiwen"
+    family-names: "Li"
+  - given-names: "Yipeng"
+    family-names: "Hu"
+  - given-names: "Yunguan"
+    family-names: "Fu"
+  - given-names: "Benjamin"
+    family-names: "Gorman"
+  - given-names: "Hans"
+    family-names: "Johnson"
+  - given-names: "Brad"
+    family-names: "Genereaux"
+  - given-names: "Barbaros S."
+    family-names: "Erdal"
+  - given-names: "Vikash"
+    family-names: "Gupta"
+  - given-names: "Andres"
+    family-names: "Diaz-Pinto"
+  - given-names: "Andre"
+    family-names: "Dourson"
+  - given-names: "Lena"
+    family-names: "Maier-Hein"
+  - given-names: "Paul F."
+    family-names: "Jaeger"
+  - given-names: "Michael"
+    family-names: "Baumgartner"
+  - given-names: "Jayashree"
+    family-names: "Kalpathy-Cramer"
+  - given-names: "Mona"
+    family-names: "Flores"
+  - given-names: "Justin"
+    family-names: "Kirby"
+  - given-names: "Lee A.D."
+    family-names: "Cooper"
+  - given-names: "Holger R."
+    family-names: "Roth"
+  - given-names: "Daguang"
+    family-names: "Xu"
+  - given-names: "David"
+    family-names: "Bericat"
+  - given-names: "Ralf"
+    family-names: "Floca"
+  - given-names: "S. Kevin"
+    family-names: "Zhou"
+  - given-names: "Haris"
+    family-names: "Shuaib"
+  - given-names: "Keyvan"
+    family-names: "Farahani"
+  - given-names: "Klaus H."
+    family-names: "Maier-Hein"
+  - given-names: "Stephen"
+    family-names: "Aylward"
+  - given-names: "Prerna"
+    family-names: "Dogra"
+  - given-names: "Sebastien"
+    family-names: "Ourselin"
+  - given-names: "Andrew"
+    family-names: "Feng"
+  doi: "https://doi.org/10.48550/arXiv.2211.02701"
+  month: 11
+  year: 2022
+  title: "MONAI: An open-source framework for deep learning in healthcare"
+...

MONAI/source/CODE_OF_CONDUCT.md

@@ -0,0 +1,76 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+ advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+ address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+ professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at monai.contact@gmail.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

MONAI/source/CONTRIBUTING.md

@@ -0,0 +1,417 @@
+- [Introduction](#introduction)
+- [The contribution process](#the-contribution-process)
+  - [Preparing pull requests](#preparing-pull-requests)
+    1. [Checking the coding style](#checking-the-coding-style)
+    1. [Unit testing](#unit-testing)
+    1. [Building the documentation](#building-the-documentation)
+    1. [Automatic code formatting](#automatic-code-formatting)
+    1. [Adding new optional dependencies](#adding-new-optional-dependencies)
+    1. [Signing your work](#signing-your-work)
+    1. [Utility functions](#utility-functions)
+    1. [Backwards compatibility](#backwards-compatibility)
+  - [Submitting pull requests](#submitting-pull-requests)
+- [The code reviewing process (for the maintainers)](#the-code-reviewing-process)
+  - [Reviewing pull requests](#reviewing-pull-requests)
+- [Admin tasks (for the maintainers)](#admin-tasks)
+  - [Releasing a new version](#release-a-new-version)
+
+## Introduction
+
+Welcome to Project MONAI! We're excited you're here and want to contribute. This documentation is intended for individuals and institutions interested in contributing to MONAI. MONAI is an open-source project and, as such, its success relies on its community of contributors willing to keep improving it. Your contribution will be a valued addition to the code base; we simply ask that you read this page and understand our contribution process, whether you are a seasoned open-source contributor or whether you are a first-time contributor.
+
+### Communicate with us
+
+We are happy to talk with you about your needs for MONAI and your ideas for contributing to the project. One way to do this is to create an issue discussing your thoughts. It might be that a very similar feature is under development or already exists, so an issue is a great starting point. If you are looking for an issue to resolve that will help Project MONAI, see the [*good first issue*](https://github.com/Project-MONAI/MONAI/labels/good%20first%20issue) and [*Contribution wanted*](https://github.com/Project-MONAI/MONAI/labels/Contribution%20wanted) labels.
+
+### Does it belong in PyTorch instead of MONAI?
+
+MONAI is part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/), and mainly based on the PyTorch and Numpy libraries. These libraries implement what we consider to be best practice for general scientific computing and deep learning functionality. MONAI builds on these with a strong focus on medical applications. As such, it is a good idea to consider whether your functionality is medical-application specific or not. General deep learning functionality may be better off in PyTorch; you can find their contribution guidelines [here](https://pytorch.org/docs/stable/community/contribution_guide.html).
+
+## The contribution process
+
+*Pull request early*
+
+We encourage you to create pull requests early. It helps us track the contributions under development, whether they are ready to be merged or not. [Create a draft pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/changing-the-stage-of-a-pull-request) until it is ready for formal review.
+
+Please note that, as per PyTorch, MONAI uses American English spelling. This means classes and variables should be: normali**z**e, visuali**z**e, colo~~u~~r, etc.
+
+### Preparing pull requests
+
+To ensure the code quality, MONAI relies on several linting tools ([flake8 and its plugins](https://gitlab.com/pycqa/flake8), [black](https://github.com/psf/black), [isort](https://github.com/timothycrosley/isort), [ruff](https://github.com/astral-sh/ruff)),
+static type analysis tools ([mypy](https://github.com/python/mypy), [pytype](https://github.com/google/pytype)), as well as a set of unit/integration tests.
+
+This section highlights all the necessary preparation steps required before sending a pull request.
+To collaborate efficiently, please read through this section and follow them.
+
+- [Checking the coding style](#checking-the-coding-style)
+- [Licensing information](#licensing-information)
+- [Unit testing](#unit-testing)
+- [Building documentation](#building-the-documentation)
+- [Signing your work](#signing-your-work)
+
+#### Checking the coding style
+
+Coding style is checked and enforced by flake8, black, isort, and ruff, using [a flake8 configuration](./setup.cfg) similar to [PyTorch's](https://github.com/pytorch/pytorch/blob/master/.flake8).
+Before submitting a pull request, we recommend that all linting should pass, by running the following command locally:
+
+```bash
+# optionally update the dependencies and dev tools
+python -m pip install -U pip
+python -m pip install -U -r requirements-dev.txt
+
+# run the linting and type checking tools
+./runtests.sh --codeformat
+
+# try to fix the coding style errors automatically
+./runtests.sh --autofix
+```
+
+Full linting and type checking may take some time. If you need a quick check, run
+
+```bash
+# run ruff only
+./runtests.sh --ruff
+```
+
+#### Licensing information
+
+All source code files should start with this paragraph:
+
+```
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+```
+
+##### Exporting modules
+
+If you intend for any variables/functions/classes to be available outside of the file with the edited functionality, then:
+
+- Create or append to the `__all__` variable (in the file in which functionality has been added), and
+- Add to the `__init__.py` file.
+
+#### Unit testing
+
+MONAI tests are located under `tests/`.
+
+- The unit test's file name currently follows `test_[module_name].py` or `test_[module_name]_dist.py`.
+- The `test_[module_name]_dist.py` subset of unit tests requires a distributed environment to verify the module with distributed GPU-based computation.
+- The integration test's file name follows `test_integration_[workflow_name].py`.
+
+A bash script (`runtests.sh`) is provided to run all tests locally.
+Please run ``./runtests.sh -h`` to see all options.
+
+To run a particular test, for example `tests/losses/test_dice_loss.py`:
+
+```
+python -m tests.losses.test_dice_loss
+```
+
+Before submitting a pull request, we recommend that all linting and unit tests
+should pass, by running the following command locally:
+
+```bash
+./runtests.sh -f -u --net --coverage
+```
+
+or (for new features that would not break existing functionality):
+
+```bash
+./runtests.sh --quick --unittests
+```
+
+It is recommended that the new test `test_[module_name].py` is constructed by using only
+python 3.9+ build-in functions, `torch`, `numpy`, `coverage` (for reporting code coverages) and `parameterized` (for organising test cases) packages.
+If it requires any other external packages, please make sure:
+
+- the packages are listed in [`requirements-dev.txt`](requirements-dev.txt)
+- the new test `test_[module_name].py` is added to the `exclude_cases` in [`./tests/min_tests.py`](./tests/min_tests.py) so that
+the minimal CI runner will not execute it.
+
+##### Testing data
+
+Testing data such as images and binary files should not be placed in the source code repository.
+Please deploy them to a reliable file sharing location (the current preferred one is [https://github.com/Project-MONAI/MONAI-extra-test-data/releases](https://github.com/Project-MONAI/MONAI-extra-test-data/releases)).
+At test time, the URLs within `tests/testing_data/data_config.json` are accessible
+via the APIs provided in `tests.utils`: `tests.utils.testing_data_config` and `tests.utils.download_url_or_skip_test`.
+
+*If it's not tested, it's broken*
+
+All new functionality should be accompanied by an appropriate set of tests.
+MONAI functionality has plenty of unit tests from which you can draw inspiration,
+and you can reach out to us if you are unsure of how to proceed with testing.
+
+MONAI's code coverage report is available at [CodeCov](https://codecov.io/gh/Project-MONAI/MONAI).
+
+#### Building the documentation
+
+MONAI's documentation is located at `docs/`.
+
+```bash
+# install the doc-related dependencies
+pip install --upgrade pip
+pip install -r docs/requirements.txt
+
+# build the docs
+cd docs/
+make html
+```
+
+The above commands build html documentation, they are used to automatically generate [https://docs.monai.io](https://docs.monai.io).
+
+The Python code docstring are written in
+[reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) and
+the documentation pages can be in either [reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) or [Markdown](https://en.wikipedia.org/wiki/Markdown).  In general the Python docstrings follow the [Google style](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings).
+
+Before submitting a pull request, it is recommended to:
+
+- edit the relevant `.rst` files in [`docs/source`](./docs/source) accordingly.
+- build html documentation locally
+- check the auto-generated documentation (by browsing `./docs/build/html/index.html` with a web browser)
+- type `make clean` in `docs/` folder to remove the current build files.
+
+Please type `make help` in `docs/` folder for all supported format options.
+
+#### Automatic code formatting
+
+MONAI provides support of automatic Python code formatting via [a customised GitHub action](https://github.com/Project-MONAI/monai-code-formatter).
+This makes the project's Python coding style consistent and reduces maintenance burdens.
+Commenting a pull request with `/black` triggers the formatting action based on [`psf/Black`](https://github.com/psf/black) (this is implemented with [`slash command dispatch`](https://github.com/marketplace/actions/slash-command-dispatch)).
+
+Steps for the formatting process:
+
+- After submitting a pull request or push to an existing pull request,
+make a comment to the pull request to trigger the formatting action.
+The first line of the comment must be `/black` so that it will be interpreted by [the comment parser](https://github.com/marketplace/actions/slash-command-dispatch#how-are-comments-parsed-for-slash-commands).
+- [Auto] The GitHub action tries to format all Python files (using [`psf/Black`](https://github.com/psf/black)) in the branch and makes a commit under the name "MONAI bot" if there's code change. The actual formatting action is deployed at [project-monai/monai-code-formatter](https://github.com/Project-MONAI/monai-code-formatter).
+- [Auto] After the formatting commit, the GitHub action adds an emoji to the comment that triggered the process.
+- Repeat the above steps if necessary.
+
+#### Adding new optional dependencies
+
+In addition to the minimal requirements of PyTorch and Numpy, MONAI's core modules are built optionally based on 3rd-party packages.
+The current set of dependencies is listed in [installing dependencies](https://monai.readthedocs.io/en/stable/installation.html#installing-the-recommended-dependencies).
+
+To allow for flexible integration of MONAI with other systems and environments,
+the optional dependency APIs are always invoked lazily. For example,
+
+```py
+from monai.utils import optional_import
+itk, _ = optional_import("itk", ...)
+
+class ITKReader(ImageReader):
+    ...
+    def read(self, ...):
+        return itk.imread(...)
+```
+
+The availability of the external `itk.imread` API is not required unless `monai.data.ITKReader.read` is called by the user.
+Integration tests with minimal requirements are deployed to ensure this strategy.
+
+To add new optional dependencies, please communicate with the core team during pull request reviews,
+and add the necessary information (at least) to the following files:
+
+- [setup.cfg](https://github.com/Project-MONAI/MONAI/blob/dev/setup.cfg)  (for package's `[options.extras_require]` config)
+- [requirements-dev.txt](https://github.com/Project-MONAI/MONAI/blob/dev/requirements-dev.txt) (pip requirements file)
+- [docs/requirements.txt](https://github.com/Project-MONAI/MONAI/blob/dev/docs/requirements.txt) (docs pip requirements file)
+- [environment-dev.yml](https://github.com/Project-MONAI/MONAI/blob/dev/environment-dev.yml) (conda environment file)
+- [installation.md](https://github.com/Project-MONAI/MONAI/blob/dev/docs/source/installation.md) (documentation)
+
+When writing unit tests that use 3rd-party packages, it is a good practice to always consider
+an appropriate fallback default behaviour when the packages are not installed in
+the testing environment. For example:
+
+```py
+from monai.utils import optional_import
+plt, has_matplotlib = optional_import("matplotlib.pyplot")
+
+@skipUnless(has_matplotlib, "Matplotlib required")
+class TestBlendImages(unittest.TestCase):
+```
+
+It skips the test cases when `matplotlib.pyplot` APIs are not available.
+
+Alternatively, add the test file name to the ``exclude_cases`` in `tests/min_tests.py` to completely skip the test
+cases when running in a minimal setup.
+
+#### Signing your work
+
+MONAI enforces the [Developer Certificate of Origin](https://developercertificate.org/) (DCO) on all pull requests.
+All commit messages should contain the `Signed-off-by` line with an email address. The [GitHub DCO app](https://github.com/apps/dco) is deployed on MONAI. The pull request's status will be `failed` if commits do not contain a valid `Signed-off-by` line.
+
+Git has a `-s` (or `--signoff`) command-line option to append this automatically to your commit message:
+
+```bash
+git commit -s -m 'a new commit'
+```
+
+The commit message will be:
+
+```
+    a new commit
+
+    Signed-off-by: Your Name <yourname@example.org>
+```
+
+Full text of the DCO:
+
+```
+Developer Certificate of Origin
+Version 1.1
+
+Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
+1 Letterman Drive
+Suite D4700
+San Francisco, CA, 94129
+
+Everyone is permitted to copy and distribute verbatim copies of this
+license document, but changing it is not allowed.
+
+
+Developer's Certificate of Origin 1.1
+
+By making a contribution to this project, I certify that:
+
+(a) The contribution was created in whole or in part by me and I
+    have the right to submit it under the open source license
+    indicated in the file; or
+
+(b) The contribution is based upon previous work that, to the best
+    of my knowledge, is covered under an appropriate open source
+    license and I have the right under that license to submit that
+    work with modifications, whether created in whole or in part
+    by me, under the same open source license (unless I am
+    permitted to submit under a different license), as indicated
+    in the file; or
+
+(c) The contribution was provided directly to me by some other
+    person who certified (a), (b) or (c) and I have not modified
+    it.
+
+(d) I understand and agree that this project and the contribution
+    are public and that a record of the contribution (including all
+    personal information I submit with it, including my sign-off) is
+    maintained indefinitely and may be redistributed consistent with
+    this project or the open source license(s) involved.
+```
+
+#### Utility functions
+
+MONAI provides a set of generic utility functions and frequently used routines.
+These are located in [``monai/utils``](./monai/utils/) and in the module folders such as [``networks/utils.py``](./monai/networks/).
+Users are encouraged to use these common routines to improve code readability and reduce the code maintenance burdens.
+
+Notably,
+
+- ``monai.module.export`` decorator can make the module name shorter when importing,
+for example, ``import monai.transforms.Spacing`` is the equivalent of ``monai.transforms.spatial.array.Spacing`` if
+``class Spacing`` defined in file `monai/transforms/spatial/array.py` is decorated with ``@export("monai.transforms")``.
+
+For string definition, [f-string](https://www.python.org/dev/peps/pep-0498/) is recommended to use over `%-print` and `format-print`. So please try to use `f-string` if you need to define any string object.
+
+#### Backwards compatibility
+
+MONAI in general follows [PyTorch's policy for backward compatibility](https://github.com/pytorch/pytorch/wiki/PyTorch's-Python-Frontend-Backward-and-Forward-Compatibility-Policy).
+Utility functions are provided in `monai.utils.deprecated` to help migrate from the deprecated to new APIs. The use of these utilities is encouraged.
+The pull request [template contains checkboxes](https://github.com/Project-MONAI/MONAI/blame/dev/.github/pull_request_template.md#L11-L12) that
+the contributor should use accordingly to clearly indicate breaking changes.
+
+The process of releasing backwards incompatible API changes is as follows:
+
+1. discuss the breaking changes during pull requests or in dev meetings with a feature proposal if needed.
+1. add a warning message in the upcoming release (version `X.Y`), the warning message should include a forecast of removing the deprecated API in:
+   1. `X+1.0` -- major version `X+1` and minor version `0` the next major version if it's a significant change,
+   1. `X.Y+2` -- major version `X` and minor version `Y+2` (the minor version after the next one), if it's a minor API change.
+   1. Note that the versioning policy is similar to PyTorch's approach which does not precisely follow [the semantic versioning](https://semver.org/) definition.
+      Major version numbers are instead used to represent major product version (which is currently not planned to be greater than 1),
+      minor version for both compatible and incompatible, and patch version for bug fixes.
+   1. when recommending new API to use in place of a deprecated API, the recommended version should
+      provide exact feature-like behaviour otherwise users will have a harder time migrating.
+1. add new test cases by extending the existing unit tests to cover both the deprecated and updated APIs.
+1. collect feedback from the users during the subsequent few releases, and reconsider step 1 if needed.
+1. before each release, review the deprecating APIs and relevant tests, and clean up the removed APIs described in step 2.
+
+### Submitting pull requests
+
+All code changes to the dev branch must be done via [pull requests](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/proposing-changes-to-your-work-with-pull-requests).
+
+1. Create a new ticket or take a known ticket from [the issue list][monai issue list].
+1. Check if there's already a branch dedicated to the task.
+1. If the task has not been taken, [create a new branch in your fork](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork)
+of the codebase named `[ticket_id]-[task_name]`.
+For example, branch name `19-ci-pipeline-setup` corresponds to [issue #19](https://github.com/Project-MONAI/MONAI/issues/19).
+Ideally, the new branch should be based on the latest `dev` branch.
+1. Make changes to the branch ([use detailed commit messages if possible](https://chris.beams.io/posts/git-commit/)).
+1. Make sure that new tests cover the changes and the changed codebase [passes all tests locally](#unit-testing).
+1. [Create a new pull request](https://help.github.com/en/desktop/contributing-to-projects/creating-a-pull-request) from the task branch to the dev branch, with detailed descriptions of the purpose of this pull request.
+1. Check [the CI/CD status of the pull request][github ci], make sure all CI/CD tests passed.
+1. Wait for reviews; if there are reviews, make point-to-point responses, make further code changes if needed.
+1. If there are conflicts between the pull request branch and the dev branch, pull the changes from the dev and resolve the conflicts locally.
+1. Reviewer and contributor may have discussions back and forth until all comments addressed.
+1. Wait for the pull request to be merged.
+
+## The code reviewing process
+
+### Reviewing pull requests
+
+All code review comments should be specific, constructive, and actionable.
+
+1. Check [the CI/CD status of the pull request][github ci], make sure all CI/CD tests passed before reviewing (contact the branch owner if needed).
+1. Read carefully the descriptions of the pull request and the files changed, write comments if needed.
+1. Make in-line comments to specific code segments, [request for changes](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-request-reviews) if needed.
+1. Review any further code changes until all comments addressed by the contributors.
+1. Comment to trigger `/black` and/or `/integration-test` for optional auto code formatting and [integration tests](.github/workflows/integration.yml).
+1. [Maintainers] Review the changes and comment `/build` to trigger internal full tests.
+1. Merge the pull request to the dev branch.
+1. Close the corresponding task ticket on [the issue list][monai issue list].
+
+[github ci]: https://github.com/Project-MONAI/MONAI/actions
+[monai issue list]: https://github.com/Project-MONAI/MONAI/issues
+
+## Admin tasks
+
+### Release a new version
+
+The `dev` branch's `HEAD` always corresponds to MONAI docker image's latest tag: `projectmonai/monai:latest`.
+The `main` branch's `HEAD` always corresponds to the latest MONAI milestone release.
+
+When major features are ready for a milestone, to prepare for a new release:
+
+- Prepare [a release note](https://github.com/Project-MONAI/MONAI/releases) and release checklist.
+- Check out or cherry-pick a new branch `releasing/[version number]` locally from the `dev` branch and push to the codebase.
+- Create a release candidate tag, for example, `git tag -a 0.1.0rc1 -m "release candidate 1 of version 0.1.0"`.
+- Push the tag to the codebase, for example, `git push origin 0.1.0rc1`.
+  This step will trigger package building and testing.
+  The resultant packages are automatically uploaded to
+  [TestPyPI](https://test.pypi.org/project/monai/).  The packages are also available for downloading as
+  repository's artifacts (e.g. the file at <https://github.com/Project-MONAI/MONAI/actions/runs/66570977>).
+- Check the release test at [TestPyPI](https://test.pypi.org/project/monai/), download the artifacts when the CI finishes.
+- Optionally run [the cron testing jobs](https://github.com/Project-MONAI/MONAI/blob/dev/.github/workflows/cron.yml) on `releasing/[version number]`.
+- Rebase `releasing/[version number]` to `main`, make sure all the test pipelines succeed.
+- Once the release candidate is verified, tag and push a milestone, for example, `git push origin 0.1.0`.
+  The tag must be with the latest commit of `releasing/[version number]`.
+- Upload the packages to [PyPI](https://pypi.org/project/monai/).
+  This could be done manually by ``twine upload dist/*``, given the artifacts are unzipped to the folder ``dist/``.
+- Merge `releasing/[version number]` to `dev`, this step must make sure that the tagging commit unchanged on `dev`.
+- Publish the release note.
+
+Note that the release should be tagged with a [PEP440](https://www.python.org/dev/peps/pep-0440/) compliant version number.
+
+If any error occurs during the release process, first check out a new hotfix branch from the `releasing/[version number]`,
+then make PRs to the `releasing/[version number]` to fix the bugs via the regular contribution procedure.
+
+If any error occurs after the release process, first check out a new hotfix branch from the `main` branch,
+make a patch version release following the semantic versioning, for example, `releasing/0.1.1`.
+Make sure the `releasing/0.1.1` is merged back into both `dev` and `main` and all the test pipelines succeed.
+
+<p align="right">
+  <a href="#introduction">⬆️ Back to Top</a>
+</p>

MONAI/source/Dockerfile

@@ -0,0 +1,66 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# To build with a different base image
+# please run `docker build` using the `--build-arg PYTORCH_IMAGE=...` flag.
+ARG PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.10-py3
+FROM ${PYTORCH_IMAGE}
+
+LABEL maintainer="monai.contact@gmail.com"
+
+# TODO: remark for issue [revise the dockerfile](https://github.com/zarr-developers/numcodecs/issues/431)
+RUN if [[ $(uname -m) =~ "aarch64" ]]; then \
+      export CFLAGS="-O3" && \
+      export DISABLE_NUMCODECS_SSE2=true && \
+      export DISABLE_NUMCODECS_AVX2=true && \
+      pip install numcodecs; \
+    fi
+
+WORKDIR /opt/monai
+
+# install full deps
+COPY requirements.txt requirements-min.txt requirements-dev.txt /tmp/
+RUN cp /tmp/requirements.txt /tmp/req.bak \
+  && awk '!/torch/' /tmp/requirements.txt > /tmp/tmp && mv /tmp/tmp /tmp/requirements.txt \
+  && python -m pip install --upgrade --no-cache-dir pip \
+  && python -m pip install --no-cache-dir -r /tmp/requirements-dev.txt
+
+# compile ext and remove temp files
+# TODO: remark for issue [revise the dockerfile #1276](https://github.com/Project-MONAI/MONAI/issues/1276)
+# please specify exact files and folders to be copied -- else, basically always, the Docker build process cannot cache
+# this or anything below it and always will build from at most here; one file change leads to no caching from here on...
+
+COPY LICENSE CHANGELOG.md CODE_OF_CONDUCT.md CONTRIBUTING.md README.md versioneer.py setup.py setup.cfg runtests.sh MANIFEST.in ./
+COPY tests ./tests
+COPY monai ./monai
+
+# TODO: remove this line and torch.patch for 24.11
+RUN patch -R -d /usr/local/lib/python3.10/dist-packages/torch/onnx/ < ./monai/torch.patch
+
+RUN BUILD_MONAI=1 FORCE_CUDA=1 python setup.py develop \
+  && rm -rf build __pycache__
+
+# NGC Client
+WORKDIR /opt/tools
+ARG NGC_CLI_URI="https://ngc.nvidia.com/downloads/ngccli_linux.zip"
+RUN wget -q ${NGC_CLI_URI} && unzip ngccli_linux.zip && chmod u+x ngc-cli/ngc && \
+    find ngc-cli/ -type f -exec md5sum {} + | LC_ALL=C sort | md5sum -c ngc-cli.md5 && \
+    rm -rf ngccli_linux.zip ngc-cli.md5
+ENV PATH=${PATH}:/opt/tools:/opt/tools/ngc-cli
+RUN apt-get update \
+  && DEBIAN_FRONTEND="noninteractive" apt-get install -y libopenslide0  \
+  && rm -rf /var/lib/apt/lists/*
+# append /opt/tools to runtime path for NGC CLI to be accessible from all file system locations
+ENV PATH=${PATH}:/opt/tools
+ENV POLYGRAPHY_AUTOINSTALL_DEPS=1
+
+
+WORKDIR /opt/monai

MONAI/source/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

MONAI/source/MANIFEST.in

@@ -0,0 +1,5 @@
+include versioneer.py
+include monai/_version.py
+
+include README.md
+include LICENSE

MONAI/source/README.md

@@ -0,0 +1,96 @@
+<p align="center">
+<img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'>
+</p>
+
+**M**edical **O**pen **N**etwork for **AI**
+
+![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg)
+[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![auto-commit-msg](https://img.shields.io/badge/dynamic/json?label=citations&query=%24.citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2FDOI%3A10.48550%2FarXiv.2211.02701%3Ffields%3DcitationCount)](https://arxiv.org/abs/2211.02701)
+[![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai)
+[![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai)
+[![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai)
+
+[![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)
+[![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)
+[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://monai.readthedocs.io/en/latest/)
+[![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI)
+[![monai Downloads Last Month](https://assets.piptrends.com/get-last-month-downloads-badge/monai.svg 'monai Downloads Last Month by pip Trends')](https://piptrends.com/package/monai)
+
+MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of the [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
+Its ambitions are as follows:
+
+- Developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
+- Creating state-of-the-art, end-to-end training workflows for healthcare imaging;
+- Providing researchers with the optimized and standardized way to create and evaluate deep learning models.
+
+## Features
+
+> _Please see [the technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) of the milestone releases._
+
+- flexible pre-processing for multi-dimensional medical imaging data;
+- compositional & portable APIs for ease of integration in existing workflows;
+- domain-specific implementations for networks, losses, evaluation metrics and more;
+- customizable design for varying user expertise;
+- multi-GPU multi-node data parallelism support.
+
+## Requirements
+
+MONAI works with the [currently supported versions of Python](https://devguide.python.org/versions), and depends directly on NumPy and PyTorch with many optional dependencies.
+
+* Major releases of MONAI will have dependency versions stated for them. The current state of the `dev` branch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so.
+* PyTorch support covers [the current version](https://github.com/pytorch/pytorch/releases) plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release.
+* Our support policy for other dependencies adheres for the most part to [SPEC0](https://scientific-python.org/specs/spec-0000), where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported.
+* See the `requirements*.txt` files for dependency version information.
+
+## Installation
+
+To install [the current release](https://pypi.org/project/monai/), you can simply run:
+
+```bash
+pip install monai
+```
+
+Please refer to [the installation guide](https://monai.readthedocs.io/en/latest/installation.html) for other installation options.
+
+## Getting Started
+
+[MedNIST demo](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/2d_classification/mednist_tutorial.ipynb) and [MONAI for PyTorch Users](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/developer_guide.ipynb) are available on Colab.
+
+Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials).
+
+Technical documentation is available at [docs.monai.io](https://docs.monai.io).
+
+## Citation
+
+If you have used MONAI in your research, please cite us! The citation can be exported from: <https://arxiv.org/abs/2211.02701>.
+
+## Model Zoo
+
+[The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community.
+Utilizing [the MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.
+
+## Contributing
+
+For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md).
+
+## Community
+
+Join the conversation on Twitter/X [@ProjectMONAI](https://twitter.com/ProjectMONAI), [LinkedIn](https://www.linkedin.com/company/projectmonai), or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9).
+
+Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions).
+
+## Links
+
+- Website: <https://project-monai.github.io/>
+- API documentation (milestone): <https://monai.readthedocs.io/>
+- API documentation (latest dev): <https://monai.readthedocs.io/en/latest/>
+- Code: <https://github.com/Project-MONAI/MONAI>
+- Project tracker: <https://github.com/Project-MONAI/MONAI/projects>
+- Issue tracker: <https://github.com/Project-MONAI/MONAI/issues>
+- Wiki: <https://github.com/Project-MONAI/MONAI/wiki>
+- Test status: <https://github.com/Project-MONAI/MONAI/actions>
+- PyPI package: <https://pypi.org/project/monai/>
+- conda-forge: <https://anaconda.org/conda-forge/monai>
+- Weekly previews: <https://pypi.org/project/monai-weekly/>
+- Docker Hub: <https://hub.docker.com/r/projectmonai/monai>

MONAI/source/SECURITY.md

@@ -0,0 +1,18 @@
+# Security Policy
+
+## Reporting a Vulnerability
+MONAI takes security seriously and appreciate your efforts to responsibly disclose vulnerabilities. If you discover a security issue, please report it as soon as possible.
+
+To report a security issue:
+* please use the GitHub Security Advisories tab to "[Open a draft security advisory](https://github.com/Project-MONAI/MONAI/security/advisories/new)".
+* Include a detailed description of the issue, steps to reproduce, potential impact, and any possible mitigations.
+* If applicable, please also attach proof-of-concept code or screenshots.
+* We aim to acknowledge your report within 72 hours and provide a status update as we investigate.
+* Please do not create public issues for security-related reports.
+
+## Disclosure Policy
+* We follow a coordinated disclosure approach.
+* We will not publicly disclose vulnerabilities until a fix has been developed and released.
+* Credit will be given to researchers who responsibly disclose vulnerabilities, if requested.
+## Acknowledgements
+We greatly appreciate contributions from the security community and strive to recognize all researchers who help keep MONAI safe.

MONAI/source/__init__.py

@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+"""
+MONAI Project Package Initialization File
+"""

MONAI/source/docs/.readthedocs.yaml

@@ -0,0 +1,14 @@
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file for details
+
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.9"
+sphinx:
+   configuration: docs/source/conf.py
+python:
+   install:
+   - requirements: docs/requirements.txt

MONAI/source/docs/Makefile

@@ -0,0 +1,29 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# https://github.com/Project-MONAI/MONAI/issues/4354
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION := python
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	PIP_ROOT_USER_ACTION=ignore pip install -r requirements.txt
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+clean:
+	rm -rf build/
+	rm -rf source/_gen
+	rm -rf source/*_properties.csv

MONAI/source/docs/_static/custom.css

@@ -0,0 +1,4 @@
+@import url('https://fonts.googleapis.com/css?family=Lekton:700|Roboto&display=swap');
+body{font-family:'Roboto',sans-serif;}.wy-menu-vertical p.caption{color:#7cccc7;}
+*{font-variant-ligatures: none;}.autoclasstoc td {padding:0.2rem;line-height:normal;}
+dl.field-list>dt{word-break: normal}