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Doctor LRP: Explainable AI for Medical Imaging

Welcome to Doctor LRP, a robust implementation of Layer-wise Relevance Propagation (LRP) paired with object detection, designed to enhance explainability in medical imaging. This project focuses on detecting brain tumors and Alzheimer's disease using CT and MRI scans, providing interpretable diagnostics through Explainable AI (XAI).

Key Features

  • Layer-wise Relevance Propagation (LRP): Gain interpretable insights into neural network predictions for medical scans.
  • Object Detection: Pinpoints areas of concern (e.g., tumor regions) in CT/MRI scans using bounding boxes.
  • Disease Classification: Accurately detects brain tumors and Alzheimer's disease from medical images.
  • Explainability in AI: Bridges the gap between AI predictions and clinical understanding by visualizing feature relevance.

Use Cases

  • Brain Tumor Detection: Identify tumor regions in CT/MRI scans and explain predictions with LRP.
  • Alzheimer's Disease Analysis: Detect early signs of Alzheimer's using MRI scans, with transparent model decision-making.
  • Medical Imaging Research: A valuable tool for researchers exploring explainable AI in healthcare.

Technologies and Tools

  • Python: Core programming language.
  • PyTorch: Framework for building and training deep learning models.
  • OpenCV: For image processing and object detection.
  • LRP Library: Custom implementation for interpretability.
  • Matplotlib: Visualization of heatmaps and object detection results.

Installation

  1. Clone the repository: 
    git clone https://github.com/your-username/Doctor-LRP.git
  2. Navigate to the project directory: 
    cd Doctor-LRP
  3. Install dependencies: 
    pip install -r requirements.txt

Output

  • Heatmaps: Visual representations of feature relevance for model predictions.
  • Bounding Boxes: Highlight regions of interest (e.g., potential tumor locations).
  • Explainable Insights: A clear understanding of "what" the model predicts and "why."

Contributing

We welcome contributions! If you want to improve this repository or add features, feel free to:

  1. Fork the repository.
  2. Create a new branch.
  3. Submit a pull request.

Keywords for Search Optimization

  • Layer-wise Relevance Propagation (LRP)
  • Explainable AI (XAI) in healthcare
  • Brain tumor detection in CT/MRI
  • Alzheimer's disease classification with MRI
  • Object detection in medical imaging
  • LRP implementation for medical AI
  • Interpretable deep learning for healthcare

License

This project is licensed under the MIT License.

Acknowledgments

Doctor LRP is inspired by the need for transparency in AI-assisted medical diagnostics, ensuring better trust and collaboration between AI systems and healthcare professionals.

Feel free to star ⭐ the repository if you find it useful! Let's make healthcare AI transparent and trustworthy!