TumorImagingBench

📋 Overview

TumorImagingBench is a robust platform that enables researchers and practitioners to:

• Extract meaningful features from medical images using state-of-the-art foundation models
• Compare performance metrics across diverse radiomics datasets
• Systematically evaluate model stability, robustness, and interpretability
• Benchmark novel foundation models against established approaches

This framework bridges the gap between advancing foundation models and their practical application in medical imaging analysis.

🔍 Key Features

• Unified Interface: Common API for all foundation model extractors
• Comprehensive Evaluation: Standardized metrics across multiple datasets
• Interpretability Tools: Generation of saliency maps and attribution analysis
• Extensible Architecture: Easily integrate new models and datasets

📂 Repository Structure

FM-extractors-radiomics/
├── models/              # Foundation model implementations
├── notebooks/           
│   ├── modelling/       # Dataset-specific modeling notebooks
│   └── analysis/        # Performance, robustness, and stability analysis
├── scripts/             # Utility scripts for batch processing
├── data/                # Dataset directory (not tracked in git)
├── utils/               # Utility functions for data processing
└── evaluation/          # Evaluation metrics and protocols

🧠 Supported Foundation Models

📊 Supported Datasets

• LUNA16: Lung Nodule Analysis
• DLCS: Duke Lung Cancer Dataset
• NSCLC Radiomics: Non-Small Cell Lung Cancer
• NSCLC Radiogenomics: Radiogenomic Analysis of NSCLC
• C4KC-KiTs: Clear Cell Renal Cell Carcinoma Kidney Tumor Segmentation
• Colorectal Liver Metastases: Liver Metastases Dataset

💻 Installation

# Create new environment
uv venv

# Activate the environment

# Clone the repository
git clone https://github.com/AIM-Harvard/TumorImagingBench.git
cd TumorImagingBench

# Install dependencies
uv sync

🚀 Usage

Feature Extraction

from models import CTClipVitExtractor, FMCIBExtractor

# Initialize a model
model = FMCIBExtractor()
model.load()

# Extract features from a sample
features = model.extract(sample_path)

For systematic feature extraction across datasets, we provide dedicated scripts in the evaluation/ directory. These scripts offer a standardized approach that can be extended to new datasets through our base feature extractor class.

Model Evaluation

For examples of model evaluation on different datasets, explore the notebooks in the notebooks/modelling/ directory. These notebooks demonstrate:

• Feature extraction workflows
• Model training and validation
• Performance analysis and comparison
• Visualization of results

📈 Analysis Tools

Our repository includes specialized analysis notebooks:

🤝 Contributing

We welcome contributions to improve this framework! Here's how you can contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow the existing code style and documentation patterns
• Add tests for new functionality
• Update documentation to reflect changes
• Ensure backward compatibility where possible

📚 Citation

If you use this framework in your research, please cite:

@article{TumorImagingBench,
  title={Foundation model embeddings for quantitative tumor imaging biomarkers},
  author={}, 
  journal={},
  year={},
  volume={},
  pages={},
  publisher={}
}

📄 License

This project is licensed under the [LICENSE NAME] - see the LICENSE file for details.