Lab-Rasool
/

sybil

@@ -1,260 +0,0 @@
----
-license: mit
-tags:
-- medical
-- cancer
-- ct-scan
-- risk-prediction
-- healthcare
-- pytorch
-- vision
-datasets:
-- NLST
-metrics:
-- auc
-- c-index
-language:
-- en
-library_name: transformers
-pipeline_tag: image-classification
----
-# Sybil - Lung Cancer Risk Prediction
-## 🎯 Model Description
-Sybil is a validated deep learning model that predicts future lung cancer risk from a single low-dose chest CT (LDCT) scan. Published in the Journal of Clinical Oncology, this model can assess cancer risk over a 1-6 year timeframe.
-### Key Features
-- **Single Scan Analysis**: Requires only one LDCT scan
-- **Multi-Year Prediction**: Provides risk scores for years 1-6
-- **Validated Performance**: Tested across multiple institutions globally
-- **Ensemble Approach**: Uses 5 models for robust predictions
-## 🚀 Quick Start
-### Installation
-```bash
-pip install huggingface-hub torch torchvision pydicom
-```
-### Basic Usage
-```python
-from huggingface_hub import snapshot_download
-import sys
-# Download model
-model_path = snapshot_download(repo_id="Lab-Rasool/sybil")
-sys.path.append(model_path)
-# Import model
-from modeling_sybil_wrapper import SybilHFWrapper
-from configuration_sybil import SybilConfig
-# Initialize
-config = SybilConfig()
-model = SybilHFWrapper(config)
-# Prepare your DICOM files (CT scan slices)
-dicom_paths = ["scan1.dcm", "scan2.dcm", ...]  # Replace with actual paths
-# Get predictions
-output = model(dicom_paths=dicom_paths)
-risk_scores = output.risk_scores.numpy()
-# Display results
-print("Lung Cancer Risk Predictions:")
-for i, score in enumerate(risk_scores):
-    print(f"Year {i+1}: {score*100:.1f}%")
-```
-## 📊 Example with Demo Data
-```python
-import requests
-import zipfile
-from io import BytesIO
-import os
-# Download demo DICOM files
-def get_demo_data():
-    cache_dir = os.path.expanduser("~/.sybil_demo")
-    demo_dir = os.path.join(cache_dir, "sybil_demo_data")
-    if not os.path.exists(demo_dir):
-        print("Downloading demo data...")
-        url = "https://www.dropbox.com/scl/fi/covbvo6f547kak4em3cjd/sybil_example.zip?rlkey=7a13nhlc9uwga9x7pmtk1cf1c&dl=1"
-        response = requests.get(url)
-        os.makedirs(cache_dir, exist_ok=True)
-        with zipfile.ZipFile(BytesIO(response.content)) as zf:
-            zf.extractall(cache_dir)
-    # Find DICOM files
-    dicom_files = []
-    for root, dirs, files in os.walk(cache_dir):
-        for file in files:
-            if file.endswith('.dcm'):
-                dicom_files.append(os.path.join(root, file))
-    return sorted(dicom_files)
-# Run demo
-from huggingface_hub import snapshot_download
-import sys
-# Load model
-model_path = snapshot_download(repo_id="Lab-Rasool/sybil")
-sys.path.append(model_path)
-from modeling_sybil_wrapper import SybilHFWrapper
-from configuration_sybil import SybilConfig
-# Initialize and predict
-config = SybilConfig()
-model = SybilHFWrapper(config)
-dicom_files = get_demo_data()
-output = model(dicom_paths=dicom_files)
-# Show results
-for i, score in enumerate(output.risk_scores.numpy()):
-    print(f"Year {i+1}: {score*100:.1f}% risk")
-```
-Expected output for demo data:
-```
-Year 1: 2.2% risk
-Year 2: 4.5% risk
-Year 3: 7.2% risk
-Year 4: 7.9% risk
-Year 5: 9.6% risk
-Year 6: 13.6% risk
-```
-## 📈 Performance Metrics
-| Dataset | 1-Year AUC | 6-Year AUC | Sample Size |
-|---------|------------|------------|-------------|
-| NLST Test | 0.94 | 0.86 | ~15,000 |
-| MGH | 0.86 | 0.75 | ~12,000 |
-| CGMH Taiwan | 0.94 | 0.80 | ~8,000 |
-## 🏥 Intended Use
-### Primary Use Cases
-- Risk stratification in lung cancer screening programs
-- Research on lung cancer prediction models
-- Clinical decision support (with appropriate oversight)
-### Users
-- Healthcare providers
-- Medical researchers
-- Screening program coordinators
-### Out of Scope
-- ❌ Diagnosis of existing cancer
-- ❌ Use with non-LDCT imaging (X-rays, MRI)
-- ❌ Sole basis for clinical decisions
-- ❌ Use outside medical supervision
-## 📋 Input Requirements
-- **Format**: DICOM files from chest CT scan
-- **Type**: Low-dose CT (LDCT)
-- **Orientation**: Axial view
-- **Order**: Anatomically ordered (abdomen → clavicles)
-- **Number of slices**: Typically 100-300 slices
-- **Resolution**: Automatically handled by model
-## ⚠️ Important Considerations
-### Medical AI Notice
-This model should **supplement, not replace**, clinical judgment. Always consider:
-- Complete patient medical history
-- Additional risk factors (smoking, family history)
-- Current clinical guidelines
-- Need for professional medical oversight
-### Limitations
-- Optimized for screening population (ages 55-80)
-- Best performance with LDCT scans
-- Not validated for pediatric use
-- Performance may vary with different scanner manufacturers
-## 📚 Citation
-If you use this model, please cite the original paper:
-```bibtex
-@article{mikhael2023sybil,
-  title={Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography},
-  author={Mikhael, Peter G and Wohlwend, Jeremy and Yala, Adam and others},
-  journal={Journal of Clinical Oncology},
-  volume={41},
-  number={12},
-  pages={2191--2200},
-  year={2023},
-  publisher={American Society of Clinical Oncology}
-}
-```
-## 🙏 Acknowledgments
-This Hugging Face implementation is based on the original work by:
-- **Original Authors**: Peter G. Mikhael & Jeremy Wohlwend
-- **Institutions**: MIT CSAIL & Massachusetts General Hospital
-- **Original Repository**: [GitHub](https://github.com/reginabarzilaygroup/Sybil)
-- **Paper**: [Journal of Clinical Oncology](https://doi.org/10.1200/JCO.22.01345)
-## 📄 License
-MIT License - See [LICENSE](LICENSE) file
-- Original Model © 2022 Peter Mikhael & Jeremy Wohlwend
-- HF Adaptation © 2024 Lab-Rasool
-## 🔧 Troubleshooting
-### Common Issues
-1. **Import Error**: Make sure to append model path to sys.path
-   ```python
-   sys.path.append(model_path)
-   ```
-2. **Missing Dependencies**: Install all requirements
-   ```bash
-   pip install torch torchvision pydicom sybil huggingface-hub
-   ```
-3. **DICOM Loading Error**: Ensure DICOM files are valid CT scans
-   ```python
-   import pydicom
-   dcm = pydicom.dcmread("your_file.dcm")  # Test single file
-   ```
-4. **Memory Issues**: Model requires ~4GB GPU memory
-   ```python
-   import torch
-   device = 'cuda' if torch.cuda.is_available() else 'cpu'
-   ```
-## 📬 Support
-- **HF Model Issues**: Open issue on this repository
-- **Original Model**: [GitHub Issues](https://github.com/reginabarzilaygroup/Sybil/issues)
-- **Medical Questions**: Consult healthcare professionals
-## 🔍 Additional Resources
-- [Original GitHub Repository](https://github.com/reginabarzilaygroup/Sybil)
-- [Paper (Open Access)](https://doi.org/10.1200/JCO.22.01345)
-- [NLST Dataset Information](https://cdas.cancer.gov/nlst/)
-- [Demo Data](https://github.com/reginabarzilaygroup/Sybil/releases)
----
-**Note**: This is a research model. Always consult qualified healthcare professionals for medical decisions.