Explainable Cross-Domain MRI Synthesis: FLAIR to T1 Translation with Pix2Pix GAN and Grad-CAM

🧠 Medical Image Translation Research Project

Abstract

This research project addresses the critical challenge of cross-domain medical image synthesis, specifically translating Fluid-Attenuated Inversion Recovery (FLAIR) magnetic resonance imaging (MRI) scans to T1-weighted MRI scans using advanced deep learning techniques.

🔬 Key Features

• Pix2Pix GAN-based Image Translation
• Explainable AI (XAI) Integration
• Advanced Medical Image Processing

📁 Repository Structure

├── mri-modal-translation.ipynb   # Primary Jupyter Notebook containing all code
├── requirements.txt               # Python dependencies
└── README.md                      # Project documentation

Quick Start

Prerequisites

• Python 3.8+
• CUDA-compatible GPU (recommended)
• Jupyter Notebook/JupyterLab

Installation

git clone https://github.com/yourusername/mri-modal-translation.git
cd mri-modal-translation
pip install -r requirements.txt

Running the Notebook

Open the Jupyter Notebook:

jupyter notebook mri-modal-translation.ipynb

🔍 Project Highlights

Methodology

• Image Translation Model: Pix2Pix Generative Adversarial Network (GAN)
• Explainability Technique: Gradient-weighted Class Activation Mapping (Grad-CAM)
• Training Optimization: Mixed-precision training

Technical Innovations

• High-fidelity medical image synthesis
• Interpretable deep learning approach
• Computational efficiency improvements

📊 Performance Metrics

The project evaluates translation quality using:

• Peak Signal-to-Noise Ratio (PSNR)
• Structural Similarity Index (SSIM)
• Mean Absolute Error (MAE)
• Dice Coefficient

Future Research and Clinical Integration

Prototype Objectives

The primary aim of this research is to develop an AI-assisted tool that:

• Augments existing medical imaging data processing
• Provides transparent, interpretable image translation
• Supports radiologists and medical professionals in diagnostic workflows

Proposed Clinical Testing and Validation

1. Collaborative Research with Medical Professionals

   • Engage radiologists and neurologists for comprehensive model evaluation
   • Conduct detailed review of Grad-CAM heatmaps to validate focus regions
   • Assess potential diagnostic value in various clinical scenarios

2. Explainable AI Focus

   • Demonstrate how the model highlights critical features of the image
   • Provide visual explanations of image translation process
   • Enable doctors to understand and verify AI-generated insights

3. Potential Clinical Applications

   • Assist in multi-modal MRI image interpretation
   • Reduce need for multiple imaging sequences
   • Provide supplementary diagnostic information

Enhanced Research Directions

• Explore alternative GAN architectures
• Extend to more medical imaging modalities
• Develop more sophisticated explainable AI techniques
• Improve robustness to varied input conditions
• Create standardized protocols for AI-assisted medical image analysis

Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

⚠️ Disclaimer

This is a research prototype designed to augment, not replace, medical professional expertise. All medical interpretations and diagnostic decisions must be performed by qualified healthcare professionals.

Contact

For research collaborations, medical insights, or technical inquiries, please open an issue in the GitHub repository.

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Research Prototype | Medical Image Processing (Computer Vision) | Explainable AI |