Release odelia_v0.6.0 - Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging
We are excited to announce the release of odelia_v0.6.0 for the swarm-learning-hpe project, focusing on significant enhancements in utilizing swarm learning for radiology image analysis. This release incorporates findings from our latest research, demonstrating the power of weakly supervised learning combined with swarm learning (SL) for effective and privacy-preserving medical AI development.
Highlights of This Release
- Integration of Weakly Supervised Learning: Leveraging case labels for tumor detection in MRI images without the need for detailed annotations, as detailed in our latest research paper.
- Advanced Swarm Learning Framework: Enhanced with the latest HPE Swarm Learning version 2.2.0, facilitating decentralized, privacy-preserving machine learning across multiple nodes without the need for raw data exchange.
- Comprehensive Medical Imaging Analysis: Support for both histopathology and radiology image analysis, including implementations for attention MIL-based models and 3D-CNN models for breast MRI examinations.
- Preprocessing Workflow Enhancement: A unified preprocessing pipeline for all datasets, ensuring optimal model training and evaluation conditions.
What's New
- Preprocessed Dataset Support: Introduction of an optional step to download preprocessed datasets for streamlined setup and testing. Preprocessing repo
- Real-World Validation: Incorporation of real-world multicentric study results, validating the 3D-ResNet101 model architecture across diverse datasets, demonstrating superior performance and generalizability
- Explainability Features: New explainability analyses, including Gradient-weighted Class Activation Mapping (GradCAM++) and Occlusion Sensitivity Analysis, provide insights into model decision-making processes.
Installation & Usage
Please refer to the Installation Guide for detailed steps on setting up the swarm learning environment. Ensure you meet the hardware and software prerequisites for optimal performance.
Quick Start
- Clone the repository and navigate to the project directory:
git clone https://github.com/KatherLab/swarm-learning-hpe.git cd swarm-learning-hpe - Follow the installation instructions to set up your swarm learning environment.
- Begin training your models using our preprocessed datasets or your data following our comprehensive Usage Guide.
Contributing
We welcome contributions from the community! If you're interested in enhancing the project or have ideas, please open an issue or submit a pull request. Check our contributing guidelines for more details.
Acknowledgments
Special thanks to our collaborators and contributors who have made this release possible. This project benefits from the findings of our recent research on integrating weakly supervised learning with swarm learning for breast MRI analysis.
License
This project is licensed under the MIT License - see the LICENSE file for details.
We hope this release will empower more researchers and practitioners in the medical imaging field to leverage the potential of swarm learning. For any questions or support, please reach out through our Issues section.