Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy - GitLite

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Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy

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🤡 Federated 🤖 Supervised 🍔 Multimodal 🍏 Retina Screening 🍎 Label Noise 🫑 Differential ✈ advanced AI 🚁 driven medical 🚀 imaging ⛱ research 🚟 that combines 🛬 the power of 🚞 Federated 🛸 Learning 🚅 Supervised 🚢 Learning ⛴ Differential 🏡 to enable 🛖 secure 🏭 collaborative 🏟 privacy 🏰 preserving 🏪 retinal 🕌 disease 🏨 detection 🚂

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🧠 Federated Self-Supervised Multimodal Retina Screening under Label Noise and Differential Privacy 👁️🔐

Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy is an advanced AI-driven medical imaging research project that combines the power of Federated Learning, Self-Supervised Learning (SSL), and Differential Privacy (DP) to enable secure, collaborative, and privacy-preserving retinal disease detection.

This project pioneers a robust framework for multimodal retinal screening using fundus and OCT images, while ensuring data confidentiality, noise tolerance, and scalable generalization across multiple healthcare institutions.

✨ Key Features

🧠 Federated Learning (FL): Train across multiple clients (hospitals/centers) without centralizing data

🧩 Self-Supervised Learning (SSL): Leverage unlabeled data using contrastive and pretext tasks for robust feature extraction

👁️ Multimodal Screening: Integrate fundus and OCT modalities for comprehensive retinal analysis

🔒 Differential Privacy (DP): Ensure patient data confidentiality with mathematical privacy guarantees

🧹 Label Noise Handling: Robust optimization and loss correction for mislabeled or uncertain clinical data

📊 Performance Metrics: AUC, Accuracy, Sensitivity, Specificity, and Explainability heatmaps (Grad-CAM, LIME)

⚙️ Scalable Design: Supports simulation of multiple clients for federated experiments

🧰 Tech Stack

Programming: Python 🐍

Frameworks: PyTorch / TensorFlow

Federated Learning: Flower (FLwr), PySyft, FedML

Self-Supervised Learning: SimCLR, MoCo, BYOL, SwAV

Privacy: Opacus (Differential Privacy for PyTorch)

Libraries: NumPy, Pandas, OpenCV, Scikit-learn, Matplotlib, Seaborn

📁 Project Structure
📁 data/             # Fundus & OCT datasets (client-wise partitions)
📁 models/           # CNN/Transformer architectures
📁 federated/        # Federated learning scripts & aggregation logic
📁 ssl/              # Self-supervised pretraining modules
📁 privacy/          # Differential privacy & noise mitigation utilities
📁 notebooks/        # Experiments, evaluation & visualization
📁 results/          # Metrics, logs, and Grad-CAM heatmaps

🚀 Getting Started
git clone https://github.com/yourusername/Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy.git
cd Federated-Self-Supervised-Multimodal-Retina-Screening-under-Label-Noise-and-Differential-Privacy
pip install -r requirements.txt
python main.py --federated --privacy --ssl

📌 Research Contributions

🏥 Privacy-Preserving Federated Retina Screening — no central data sharing required

🔬 Noise-Resilient Learning — label correction and loss reweighting improve reliability

💡 Self-Supervised Pretraining — leverages unannotated data for superior generalization

⚖️ Differential Privacy Integration — ensures compliance with healthcare data privacy laws (HIPAA, GDPR)

📈 Explainable AI (XAI): Visual interpretability for clinical trust and transparency

🧪 Evaluation Metrics
Metric	Description
AUC	Area under ROC curve for disease classification
Accuracy	Overall model performance
Sensitivity (Recall)	Correctly identified positive cases
Specificity	Correctly identified negatives
Privacy ε	Differential privacy parameter controlling data leakage
🤝 Contributing

Researchers and developers are welcome to contribute by:

Improving multimodal feature fusion

Extending SSL techniques

Enhancing differential privacy mechanisms

Conducting cross-dataset or cross-institutional evaluations

## License

[MIT License](LICENSE)