initialize the model package structure

.gitattributes

@@ -33,3 +33,4 @@ 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
+models/model.ts filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,183 @@
+---
+tags:
+- monai
+- medical
+library_name: monai
+license: apache-2.0
+---
+# Description
+A pre-trained model for segmenting nuclei cells with user clicks/interactions.
+
+![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/11.gif)
+![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/33.gif)
+![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/22.gif)
+
+# Model Overview
+This model is trained using [BasicUNet](https://docs.monai.io/en/latest/networks.html#basicunet) over [ConSeP](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet) dataset.
+
+## Data
+The training dataset is from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet
+```commandline
+wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip
+unzip -q consep_dataset.zip
+```
+![](images/dataset.jpeg)<br/>
+
+## Training configuration
+The training was performed with the following:
+
+- GPU: at least 12GB of GPU memory
+- Actual Model Input: 4 x 128 x 128
+- AMP: True
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss: DiceLoss
+
+
+### Preprocessing
+After [downloading this dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip),
+python script `data_process.py` from `scripts` folder can be used to preprocess and generate the final dataset for training.
+
+```
+python scripts/data_process.py --input /path/to/data/CoNSeP --output /path/to/data/CoNSePNuclei
+```
+
+After generating the output files, please modify the `dataset_dir` parameter specified in `configs/train.json` and `configs/inference.json` to reflect the output folder which contains new dataset.json.
+
+Class values in dataset are
+
+ - 1 = other
+ - 2 = inflammatory
+ - 3 = healthy epithelial
+ - 4 = dysplastic/malignant epithelial
+ - 5 = fibroblast
+ - 6 = muscle
+ - 7 = endothelial
+
+As part of pre-processing, the following steps are executed.
+
+ - Crop and Extract each nuclei Image + Label (128x128) based on the centroid given in the dataset.
+ - Combine classes 3 & 4 into the epithelial class and 5,6 & 7 into the spindle-shaped class.
+ - Update the label index for the target nuclie based on the class value
+ - Other cells which are part of the patch are modified to have label idex = 255
+
+Example dataset.json
+```json
+{
+  "training": [
+    {
+      "image": "/workspace/data/CoNSePNuclei/Train/Images/train_1_3_0001.png",
+      "label": "/workspace/data/CoNSePNuclei/Train/Labels/train_1_3_0001.png",
+      "nuclei_id": 1,
+      "mask_value": 3,
+      "centroid": [
+        64,
+        64
+      ]
+    }
+  ],
+  "validation": [
+    {
+      "image": "/workspace/data/CoNSePNuclei/Test/Images/test_1_3_0001.png",
+      "label": "/workspace/data/CoNSePNuclei/Test/Labels/test_1_3_0001.png",
+      "nuclei_id": 1,
+      "mask_value": 3,
+      "centroid": [
+        64,
+        64
+      ]
+    }
+  ]
+}
+```
+
+
+## Input and output formats
+### Input: 5 channels
+- 3 RGB channels
+- +ve signal channel (this nuclei)
+- -ve signal channel (other nuclei)
+
+### Output: 2 channels
+ - 0 = Background
+ - 1 = Nuclei
+
+![](images/train_in_out.jpeg)
+
+## Scores
+This model achieves the following Dice score on the validation data provided as part of the dataset:
+
+- Train Dice score = 0.89
+- Validation Dice score = 0.85
+
+
+## Training Performance
+A graph showing the training Loss and Dice over 50 epochs.
+
+![](images/train_loss.jpeg) <br>
+![](images/train_dice.jpeg) <br>
+
+## Validation Performance
+A graph showing the validation mean Dice over 50 epochs.
+
+![](images/val_dice.jpeg) <br>
+
+
+## commands example
+Execute training:
+
+```
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
+```
+
+Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
+Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
+
+Override the `train` config to execute evaluation with the trained model:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']" --logging_file configs/logging.conf
+```
+
+Execute inference:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
+```
+
+# Disclaimer
+This is an example, not to be used for diagnostic purposes.
+
+# References
+[1] Koohbanani, Navid Alemi, et al. "NuClick: a deep learning framework for interactive segmentation of microscopic images." Medical Image Analysis 65 (2020): 101771. https://arxiv.org/abs/2005.14511.
+
+[2] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019. [[doi](https://doi.org/10.1016/j.media.2019.101563)]
+
+[3] NuClick [PyTorch](https://github.com/mostafajahanifar/nuclick_torch) Implementation
+
+# License
+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.

configs/evaluate.json

@@ -0,0 +1,58 @@
+{
+    "validate#dataset#cache_rate": 0,
+    "validate#postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "sigmoid": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "threshold": 0.5
+            },
+            {
+                "_target_": "SaveImaged",
+                "_disabled_": true,
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir",
+                "output_ext": ".png"
+            }
+        ]
+    },
+    "validate#handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        },
+        {
+            "_target_": "MetricsSaver",
+            "save_dir": "@output_dir",
+            "metrics": [
+                "val_mean_dice",
+                "val_acc"
+            ],
+            "metric_details": [
+                "val_mean_dice"
+            ],
+            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
+            "summary_ops": "*"
+        }
+    ],
+    "evaluating": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@validate#evaluator.run()"
+    ]
+}

configs/inference.json

@@ -0,0 +1,130 @@
+{
+    "imports": [
+        "$import glob",
+        "$import json",
+        "$import pathlib",
+        "$import os"
+    ],
+    "bundle_root": "/workspace/data/pathology_nuclick_annotation",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/data/CoNSePNuclei",
+    "images": "$list(sorted(glob.glob(@dataset_dir + '/Test/Images/*.png')))[:1]",
+    "centroids": "$list(sorted(glob.glob(@dataset_dir + '/Test/Centroids/*.txt')))[:1]",
+    "input_data": "$[{'image': i, 'foreground': json.loads(pathlib.Path(c).read_text())} for i,c in zip(@images, @centroids)]",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "BasicUNet",
+        "spatial_dims": 2,
+        "in_channels": 5,
+        "out_channels": 1,
+        "features": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            32
+        ]
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "image",
+                "dtype": "uint8"
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": "image"
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": "image",
+                "a_min": 0.0,
+                "a_max": 255.0,
+                "b_min": -1.0,
+                "b_max": 1.0
+            },
+            {
+                "_target_": "AddClickSignalsd",
+                "image": "image",
+                "foreground": "foreground",
+                "gaussian": false
+            },
+            {
+                "_target_": "SqueezeDimd",
+                "keys": "image"
+            }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "@input_data",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 4
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "sigmoid": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": "pred",
+                "threshold": 0.5
+            },
+            {
+                "_target_": "KeepLargestConnectedComponentd",
+                "keys": "pred"
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir",
+                "output_ext": ".png"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@bundle_root + '/models/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@evaluator.run()"
+    ]
+}

configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

@@ -0,0 +1,79 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.0.1",
+    "changelog": {
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "1.0.1",
+    "pytorch_version": "1.13.0",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {
+        "nibabel": "4.0.1",
+        "pytorch-ignite": "0.4.9"
+    },
+    "task": "Pathology Nuclick segmentation",
+    "description": "A pre-trained model for Nuclei Classification within Haematoxylin & Eosin stained histology images",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "consep_dataset.zip from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet",
+    "data_type": "png",
+    "image_classes": "RGB channel data, intensity scaled to [0, 1]",
+    "label_classes": "single channel data",
+    "pred_classes": "1 channel data, with value 1 as nuclei and 0 as background",
+    "eval_metrics": {
+        "mean_dice": 0.85
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Koohbanani, Navid Alemi, et al. \"NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images.\" https://arxiv.org/abs/2005.14511",
+        "S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. \"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images.\" Medical Image Analysis, Sept. 2019. https://doi.org/10.1016/j.media.2019.101563",
+        "NuClick PyTorch Implementation, https://github.com/mostafajahanifar/nuclick_torch"
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "png",
+                "format": "RGB",
+                "modality": "regular",
+                "num_channels": 5,
+                "spatial_shape": [
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "R",
+                    "1": "G",
+                    "2": "B",
+                    "3": "+ve Signal",
+                    "4": "-ve Signal"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 1,
+                "spatial_shape": [
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "Nuclei"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_evaluate.json

@@ -0,0 +1,30 @@
+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#handlers#1#_disabled_": "$dist.get_rank() > 0",
+    "evaluating": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import logging",
+        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@validate#evaluator.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/multi_gpu_train.json

@@ -0,0 +1,41 @@
+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "train#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@train#dataset",
+        "even_divisible": true,
+        "shuffle": true
+    },
+    "train#dataloader#sampler": "@train#sampler",
+    "train#dataloader#shuffle": false,
+    "train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
+    "training": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import logging",
+        "$@train#trainer.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@train#trainer.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/train.json

@@ -0,0 +1,328 @@
+{
+    "imports": [
+        "$import glob",
+        "$import ignite",
+        "$import json",
+        "$import pathlib",
+        "$import os"
+    ],
+    "bundle_root": "/workspace/data/pathology_nuclick_annotation",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/data/CoNSePNuclei",
+    "dataset_json": "$@dataset_dir + '/dataset.json'",
+    "train_datalist": "$json.loads(pathlib.Path(@dataset_json).read_text())['training']",
+    "val_datalist": "$json.loads(pathlib.Path(@dataset_json).read_text())['validation']",
+    "val_interval": 1,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "BasicUNet",
+        "spatial_dims": 2,
+        "in_channels": 5,
+        "out_channels": 1,
+        "features": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            32
+        ]
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "DiceLoss",
+        "sigmoid": true,
+        "squared_pred": true
+    },
+    "optimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@network.parameters()",
+        "lr": 0.0001
+    },
+    "max_epochs": 50,
+    "train": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "LoadImaged",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "dtype": "uint8"
+                },
+                {
+                    "_target_": "EnsureChannelFirstd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "SplitLabeld",
+                    "keys": "label",
+                    "mask_value": "",
+                    "others_value": 255
+                },
+                {
+                    "_target_": "RandTorchVisiond",
+                    "keys": "image",
+                    "name": "ColorJitter",
+                    "brightness": 0.251,
+                    "contrast": 0.75,
+                    "saturation": 0.25,
+                    "hue": 0.04,
+                    "prob": 0.5
+                },
+                {
+                    "_target_": "RandFlipd",
+                    "keys": [
+                        "image",
+                        "label",
+                        "others"
+                    ],
+                    "prob": 0.5
+                },
+                {
+                    "_target_": "RandRotate90d",
+                    "keys": [
+                        "image",
+                        "label",
+                        "others"
+                    ],
+                    "prob": 0.5
+                },
+                {
+                    "_target_": "ScaleIntensityRanged",
+                    "keys": "image",
+                    "a_min": 0.0,
+                    "a_max": 255.0,
+                    "b_min": -1.0,
+                    "b_max": 1.0
+                },
+                {
+                    "_target_": "AddPointGuidanceSignald",
+                    "image": "image",
+                    "label": "label",
+                    "others": "others",
+                    "use_distance": true,
+                    "gaussian": false
+                },
+                {
+                    "_target_": "SelectItemsd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                }
+            ]
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "@train_datalist",
+            "transform": "@train#preprocessing",
+            "cache_rate": 1.0,
+            "num_workers": 4
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@train#dataset",
+            "batch_size": 64,
+            "shuffle": true,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "sigmoid": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": "pred",
+                    "threshold": 0.5
+                }
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "interval": "@val_interval"
+            },
+            {
+                "_target_": "StatsHandler",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "scripts.TensorBoardImageHandler",
+                "log_dir": "@output_dir",
+                "batch_limit": 4,
+                "tag_name": "train"
+            }
+        ],
+        "key_metric": {
+            "train_dice": {
+                "_target_": "monai.handlers.MeanDice",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])",
+                "include_background": false
+            }
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "max_epochs": "@max_epochs",
+            "device": "@device",
+            "train_data_loader": "@train#dataloader",
+            "network": "@network",
+            "loss_function": "@loss",
+            "optimizer": "@optimizer",
+            "inferer": "@train#inferer",
+            "postprocessing": "@train#postprocessing",
+            "key_train_metric": "@train#key_metric",
+            "train_handlers": "@train#handlers",
+            "amp": true
+        }
+    },
+    "validate": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "LoadImaged",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "dtype": "uint8"
+                },
+                {
+                    "_target_": "EnsureChannelFirstd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "SplitLabeld",
+                    "keys": "label",
+                    "mask_value": "",
+                    "others_value": 255
+                },
+                {
+                    "_target_": "ScaleIntensityRanged",
+                    "keys": "image",
+                    "a_min": 0.0,
+                    "a_max": 255.0,
+                    "b_min": -1.0,
+                    "b_max": 1.0
+                },
+                {
+                    "_target_": "AddPointGuidanceSignald",
+                    "image": "image",
+                    "label": "label",
+                    "others": "others",
+                    "use_distance": true,
+                    "gaussian": false,
+                    "drop_rate": 1.0
+                },
+                {
+                    "_target_": "SelectItemsd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                }
+            ]
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "@val_datalist",
+            "transform": "@validate#preprocessing",
+            "cache_rate": 1.0
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 64,
+            "shuffle": false,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": "%train#postprocessing",
+        "handlers": [
+            {
+                "_target_": "StatsHandler",
+                "iteration_log": false
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "iteration_log": false
+            },
+            {
+                "_target_": "CheckpointSaver",
+                "save_dir": "@ckpt_dir",
+                "save_dict": {
+                    "model": "@network"
+                },
+                "save_key_metric": true,
+                "key_metric_filename": "model.pt"
+            },
+            {
+                "_target_": "scripts.TensorBoardImageHandler",
+                "log_dir": "@output_dir",
+                "batch_limit": 8,
+                "tag_name": "val"
+            }
+        ],
+        "key_metric": {
+            "val_mean_dice": {
+                "_target_": "MeanDice",
+                "include_background": false,
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "additional_metrics": {
+            "val_accuracy": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "evaluator": {
+            "_target_": "SupervisedEvaluator",
+            "device": "@device",
+            "val_data_loader": "@validate#dataloader",
+            "network": "@network",
+            "inferer": "@validate#inferer",
+            "postprocessing": "@validate#postprocessing",
+            "key_val_metric": "@validate#key_metric",
+            "additional_metrics": "@validate#additional_metrics",
+            "val_handlers": "@validate#handlers",
+            "amp": true
+        }
+    },
+    "training": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()"
+    ]
+}

docs/README.md

@@ -0,0 +1,176 @@
+# Description
+A pre-trained model for segmenting nuclei cells with user clicks/interactions.
+
+![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/11.gif)
+![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/33.gif)
+![nuclick](https://github.com/mostafajahanifar/nuclick_torch/raw/master/docs/22.gif)
+
+# Model Overview
+This model is trained using [BasicUNet](https://docs.monai.io/en/latest/networks.html#basicunet) over [ConSeP](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet) dataset.
+
+## Data
+The training dataset is from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet
+```commandline
+wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip
+unzip -q consep_dataset.zip
+```
+![](images/dataset.jpeg)<br/>
+
+## Training configuration
+The training was performed with the following:
+
+- GPU: at least 12GB of GPU memory
+- Actual Model Input: 4 x 128 x 128
+- AMP: True
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss: DiceLoss
+
+
+### Preprocessing
+After [downloading this dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip),
+python script `data_process.py` from `scripts` folder can be used to preprocess and generate the final dataset for training.
+
+```
+python scripts/data_process.py --input /path/to/data/CoNSeP --output /path/to/data/CoNSePNuclei
+```
+
+After generating the output files, please modify the `dataset_dir` parameter specified in `configs/train.json` and `configs/inference.json` to reflect the output folder which contains new dataset.json.
+
+Class values in dataset are
+
+ - 1 = other
+ - 2 = inflammatory
+ - 3 = healthy epithelial
+ - 4 = dysplastic/malignant epithelial
+ - 5 = fibroblast
+ - 6 = muscle
+ - 7 = endothelial
+
+As part of pre-processing, the following steps are executed.
+
+ - Crop and Extract each nuclei Image + Label (128x128) based on the centroid given in the dataset.
+ - Combine classes 3 & 4 into the epithelial class and 5,6 & 7 into the spindle-shaped class.
+ - Update the label index for the target nuclie based on the class value
+ - Other cells which are part of the patch are modified to have label idex = 255
+
+Example dataset.json
+```json
+{
+  "training": [
+    {
+      "image": "/workspace/data/CoNSePNuclei/Train/Images/train_1_3_0001.png",
+      "label": "/workspace/data/CoNSePNuclei/Train/Labels/train_1_3_0001.png",
+      "nuclei_id": 1,
+      "mask_value": 3,
+      "centroid": [
+        64,
+        64
+      ]
+    }
+  ],
+  "validation": [
+    {
+      "image": "/workspace/data/CoNSePNuclei/Test/Images/test_1_3_0001.png",
+      "label": "/workspace/data/CoNSePNuclei/Test/Labels/test_1_3_0001.png",
+      "nuclei_id": 1,
+      "mask_value": 3,
+      "centroid": [
+        64,
+        64
+      ]
+    }
+  ]
+}
+```
+
+
+## Input and output formats
+### Input: 5 channels
+- 3 RGB channels
+- +ve signal channel (this nuclei)
+- -ve signal channel (other nuclei)
+
+### Output: 2 channels
+ - 0 = Background
+ - 1 = Nuclei
+
+![](images/train_in_out.jpeg)
+
+## Scores
+This model achieves the following Dice score on the validation data provided as part of the dataset:
+
+- Train Dice score = 0.89
+- Validation Dice score = 0.85
+
+
+## Training Performance
+A graph showing the training Loss and Dice over 50 epochs.
+
+![](images/train_loss.jpeg) <br>
+![](images/train_dice.jpeg) <br>
+
+## Validation Performance
+A graph showing the validation mean Dice over 50 epochs.
+
+![](images/val_dice.jpeg) <br>
+
+
+## commands example
+Execute training:
+
+```
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
+```
+
+Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
+Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
+
+Override the `train` config to execute evaluation with the trained model:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']" --logging_file configs/logging.conf
+```
+
+Execute inference:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
+```
+
+# Disclaimer
+This is an example, not to be used for diagnostic purposes.
+
+# References
+[1] Koohbanani, Navid Alemi, et al. "NuClick: a deep learning framework for interactive segmentation of microscopic images." Medical Image Analysis 65 (2020): 101771. https://arxiv.org/abs/2005.14511.
+
+[2] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019. [[doi](https://doi.org/10.1016/j.media.2019.101563)]
+
+[3] NuClick [PyTorch](https://github.com/mostafajahanifar/nuclick_torch) Implementation
+
+# License
+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.

docs/data_license.txt

@@ -0,0 +1,6 @@
+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i. HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
+   https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/

models/model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9886629a19d2f3ef2d607d156f1c9409d32d193a7b831d19df35cf4cf303aebe
+size 31162823

models/model.ts

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b519966e4571708b3cc0ca78b1178e58e26558227f988b5be3618a438ed0864
+size 31274615

scripts/__init__.py

@@ -0,0 +1,12 @@
+# 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.
+
+from .handlers import TensorBoardImageHandler

scripts/data_process.py

@@ -0,0 +1,79 @@
+# 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.
+import argparse
+import glob
+import json
+import logging
+import os
+
+from dataset import consep_nuclei_dataset
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    logging.basicConfig(
+        level=logging.INFO,
+        format="[%(asctime)s] [%(process)s] [%(threadName)s] [%(levelname)s] (%(name)s:%(lineno)d) - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        force=True,
+    )
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input",
+        "-i",
+        type=str,
+        default=r"/workspace/data/CoNSeP",
+        help="Input/Downloaded/Extracted dir for CoNSeP Dataset",
+    )
+    parser.add_argument(
+        "--output",
+        "-o",
+        type=str,
+        default=r"/workspace/data/CoNSePNuclei",
+        help="Output dir to store pre-processed data",
+    )
+    parser.add_argument("--crop_size", "-s", type=int, default=128, help="Crop size for each Nuclei")
+    parser.add_argument("--limit", "-n", type=int, default=0, help="Non-zero value to limit processing max records")
+
+    args = parser.parse_args()
+    dataset_json = {}
+    for f, v in {"Train": "training", "Test": "validation"}.items():
+        logger.info("---------------------------------------------------------------------------------")
+        if not os.path.exists(os.path.join(args.input, f)):
+            logger.warning(f"Ignore {f} (NOT Exists in Input Folder)")
+            continue
+
+        logger.info(f"Processing Images/labels for: {f}")
+        images_path = os.path.join(args.input, f, "Images", "*.png")
+        labels_path = os.path.join(args.input, f, "Labels", "*.mat")
+        images = sorted(glob.glob(images_path))
+        labels = sorted(glob.glob(labels_path))
+        ds = [{"image": i, "label": l} for i, l in zip(images, labels)]
+
+        output_dir = os.path.join(args.output, f) if args.output else f
+        crop_size = args.crop_size
+        limit = args.limit
+
+        ds_new = consep_nuclei_dataset(ds, output_dir, crop_size, limit=limit)
+        logger.info(f"Total Generated/Extended Records: {len(ds)} => {len(ds_new)}")
+
+        dataset_json[v] = ds_new
+
+    ds_file = os.path.join(args.output, "dataset.json")
+    with open(ds_file, "w") as fp:
+        json.dump(dataset_json, fp, indent=2)
+    logger.info(f"Dataset JSON Generated at: {ds_file}")
+
+
+if __name__ == "__main__":
+    main()

scripts/dataset.py

@@ -0,0 +1,189 @@
+# 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.
+
+import copy
+import json
+import logging
+import os
+import pathlib
+from typing import Dict, List
+
+import numpy as np
+from monai.apps.utils import tqdm
+from monai.utils import optional_import
+
+loadmat, _ = optional_import("scipy.io", name="loadmat")
+PILImage, _ = optional_import("PIL.Image")
+
+
+def consep_nuclei_dataset(datalist, output_dir, crop_size, min_area=80, min_distance=20, limit=0) -> List[Dict]:
+    """
+    Utility to pre-process and create dataset list for Patches per Nuclei for training over ConSeP dataset.
+
+    Args:
+        datalist: A list of data dictionary. Each entry should at least contain 'image_key': <image filename>.
+            For example, typical input data can be a list of dictionaries::
+
+                [{'image': <image filename>, 'label': <label filename>}]
+
+        output_dir: target directory to store the training data after flattening
+        crop_size: Crop Size for each patch
+        min_area: Min Area for each nuclei to be included in dataset
+        min_distance: Min Distance from boundary for each nuclei to be included in dataset
+        limit: limit number of inputs for pre-processing.  Defaults to 0 (no limit).
+
+    Raises:
+        ValueError: When ``datalist`` is Empty
+        ValueError: When ``scipy.io.loadmat`` is Not available
+
+    Returns:
+        A new datalist that contains path to the images/labels after pre-processing.
+
+    Example::
+
+        datalist = consep_nuclei_dataset(
+            datalist=[{'image': 'img1.png', 'label': 'label1.mat'}],
+            output_dir=output,
+            crop_size=128,
+            limit=1,
+        )
+
+        print(datalist[0]["image"], datalist[0]["label"])
+    """
+
+    if not len(datalist):
+        raise ValueError("Input datalist is empty")
+
+    if not loadmat:
+        print("Please make sure scipy with loadmat function is correctly installed")
+        raise ValueError("scipy.io.loadmat module/function not found")
+
+    dataset_json: List[Dict] = []
+    for d in tqdm(datalist):
+        logging.debug(f"Processing Image: {d['image']} => Label: {d['label']}")
+
+        # Image
+        image = PILImage.open(d["image"]).convert("RGB")
+
+        # Label
+        m = loadmat(d["label"])
+        instances = m["inst_map"]
+
+        for nuclei_id, (class_id, (y, x)) in enumerate(zip(m["inst_type"], m["inst_centroid"]), start=1):
+            x, y = (int(x), int(y))
+            class_id = int(class_id)
+            class_id = 3 if class_id in (3, 4) else 4 if class_id in (5, 6, 7) else class_id  # override
+
+            if 0 < limit <= len(dataset_json):
+                return dataset_json
+
+            item = __prepare_patch(
+                d=d,
+                nuclei_id=nuclei_id,
+                output_dir=output_dir,
+                image=image,
+                instances=instances,
+                instance_idx=nuclei_id,
+                crop_size=crop_size,
+                class_id=class_id,
+                centroid=(x, y),
+                min_area=min_area,
+                min_distance=min_distance,
+                others_idx=255,
+            )
+
+            if item:
+                dataset_json.append(item)
+
+    return dataset_json
+
+
+def __prepare_patch(
+    d,
+    nuclei_id,
+    output_dir,
+    image,
+    instances,
+    instance_idx,
+    crop_size,
+    class_id,
+    centroid,
+    min_area,
+    min_distance,
+    others_idx=255,
+):
+    image_np = np.array(image)
+    image_size = image.size
+
+    bbox = __compute_bbox(crop_size, centroid, image_size)
+
+    cropped_label_np = instances[bbox[0] : bbox[2], bbox[1] : bbox[3]]
+    cropped_label_np = np.array(cropped_label_np)
+
+    this_label = np.where(cropped_label_np == instance_idx, class_id, 0)
+    if np.count_nonzero(this_label) < min_area:
+        return None
+
+    x, y = centroid
+    if x < min_distance or y < min_distance or (image_size[0] - x) < min_distance or (image_size[1] - y < min_distance):
+        return None
+
+    centroid = centroid[0] - bbox[0], centroid[1] - bbox[1]
+    others = np.where(np.logical_and(cropped_label_np > 0, cropped_label_np != instance_idx), others_idx, 0)
+    cropped_label_np = this_label + others
+    cropped_label = PILImage.fromarray(cropped_label_np.astype(np.uint8), None)
+
+    cropped_image_np = image_np[bbox[0] : bbox[2], bbox[1] : bbox[3], :]
+    cropped_image = PILImage.fromarray(cropped_image_np, "RGB")
+
+    images_dir = os.path.join(output_dir, "Images") if output_dir else "Images"
+    labels_dir = os.path.join(output_dir, "Labels") if output_dir else "Labels"
+    centroids_dir = os.path.join(output_dir, "Centroids") if output_dir else "Centroids"
+
+    os.makedirs(images_dir, exist_ok=True)
+    os.makedirs(labels_dir, exist_ok=True)
+    os.makedirs(centroids_dir, exist_ok=True)
+
+    image_id = pathlib.Path(d["image"]).stem
+    file_prefix = f"{image_id}_{class_id}_{str(instance_idx).zfill(4)}"
+    image_file = os.path.join(images_dir, f"{file_prefix}.png")
+    label_file = os.path.join(labels_dir, f"{file_prefix}.png")
+    centroid_file = os.path.join(centroids_dir, f"{file_prefix}.txt")
+
+    cropped_image.save(image_file)
+    cropped_label.save(label_file)
+    with open(centroid_file, "w") as fp:
+        json.dump([centroid], fp)
+
+    item = copy.deepcopy(d)
+    item["nuclei_id"] = nuclei_id
+    item["mask_value"] = class_id
+    item["image"] = image_file
+    item["label"] = label_file
+    item["centroid"] = centroid
+    return item
+
+
+def __compute_bbox(patch_size, centroid, size):
+    x, y = centroid
+    m, n = size
+
+    x_start = int(max(x - patch_size / 2, 0))
+    y_start = int(max(y - patch_size / 2, 0))
+    x_end = x_start + patch_size
+    y_end = y_start + patch_size
+    if x_end > m:
+        x_end = m
+        x_start = m - patch_size
+    if y_end > n:
+        y_end = n
+        y_start = n - patch_size
+    return x_start, y_start, x_end, y_end

scripts/handlers.py

@@ -0,0 +1,111 @@
+# 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.
+
+import logging
+from typing import TYPE_CHECKING, Callable, Optional
+
+import numpy as np
+import torch
+import torch.distributed
+from monai.config import IgniteInfo
+from monai.utils import min_version, optional_import
+
+Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
+make_grid, _ = optional_import("torchvision.utils", name="make_grid")
+Image, _ = optional_import("PIL.Image")
+ImageDraw, _ = optional_import("PIL.ImageDraw")
+
+if TYPE_CHECKING:
+    from ignite.engine import Engine
+    from torch.utils.tensorboard import SummaryWriter
+else:
+    Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
+    SummaryWriter, _ = optional_import("torch.utils.tensorboard", name="SummaryWriter")
+
+
+class TensorBoardImageHandler:
+    def __init__(
+        self,
+        summary_writer: Optional[SummaryWriter] = None,
+        log_dir: str = "./runs",
+        tag_name="val",
+        interval: int = 1,
+        batch_transform: Callable = lambda x: x,
+        output_transform: Callable = lambda x: x,
+        batch_limit=1,
+        device=None,
+    ) -> None:
+        self.writer = SummaryWriter(log_dir=log_dir) if summary_writer is None else summary_writer
+        self.tag_name = tag_name
+        self.interval = interval
+        self.batch_transform = batch_transform
+        self.output_transform = output_transform
+        self.batch_limit = batch_limit
+        self.device = device
+
+        self.logger = logging.getLogger(__name__)
+
+        if torch.distributed.is_initialized():
+            self.tag_name = f"{self.tag_name}-r{torch.distributed.get_rank()}"
+
+    def attach(self, engine: Engine) -> None:
+        engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.interval), self, "epoch")
+
+    def __call__(self, engine: Engine, action) -> None:
+        epoch = engine.state.epoch
+        batch_data = self.batch_transform(engine.state.batch)
+        output_data = self.output_transform(engine.state.output)
+
+        self.write_images(batch_data, output_data, epoch)
+
+    def write_images(self, batch_data, output_data, epoch):
+        for bidx in range(len(batch_data)):
+            image = batch_data[bidx]["image"].detach().cpu().numpy()
+            y = output_data[bidx]["label"].detach().cpu().numpy()
+
+            tag_prefix = f"{self.tag_name} - b{bidx} - " if self.batch_limit != 1 else ""
+            img_np = image[:3]
+            img_np[0, :, :] = np.where(y[0] > 0, 1, img_np[0, :, :])
+            img_tensor = make_grid(torch.from_numpy(img_np), normalize=True)
+            self.writer.add_image(tag=f"{tag_prefix}Image", img_tensor=img_tensor, global_step=epoch)
+
+            y_pred = output_data[bidx]["pred"].detach().cpu().numpy()
+
+            cl = np.count_nonzero(y)
+            cp = np.count_nonzero(y_pred)
+            self.logger.info(
+                "{} => {} - Image: {};"
+                " Label: {} (nz: {});"
+                " Pred: {} (nz: {});"
+                " Diff: {:.2f}%;"
+                " Sig: (pos-nz: {}, neg-nz: {})".format(
+                    self.tag_name,
+                    bidx,
+                    image.shape,
+                    y.shape,
+                    cl,
+                    y_pred.shape,
+                    cp,
+                    100 * (cp - cl) / (cl + 1),
+                    np.count_nonzero(image[3]),
+                    np.count_nonzero(image[4]),
+                )
+            )
+
+            tag_prefix = f"{self.tag_name} - b{bidx} - " if self.batch_limit != 1 else f"{self.tag_name} - "
+            label_pred = [y, y_pred, image[3][None] > 0, image[4][None] > 0]
+            label_pred_tag = f"{tag_prefix}Label vs Pred vs Pos vs Neg"
+
+            img_tensor = make_grid(tensor=torch.from_numpy(np.array(label_pred)), nrow=4, normalize=True, pad_value=10)
+            self.writer.add_image(tag=label_pred_tag, img_tensor=img_tensor, global_step=epoch)
+
+            if self.batch_limit == 1 or bidx == (self.batch_limit - 1):
+                break