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🧠 Explainable Multi-modality Learning for Eye Disease Diagnosis with Missing Data

This repository provides the official PyTorch implementation for our paper: "Explainable Multi-modality Learning for Eye Disease Diagnosis with Missing Data"

🚀 Getting Started

📋 Prerequisites

Ensure your environment satisfies the following dependencies:

Python         == 3.8.5  
PyTorch        == 1.8.1  
TorchVision    == 0.9.1  
NumPy          == 1.20.2  
OpenCV (cv2)   == 4.5.1

Install dependencies via pip:

pip install torch==1.8.1 torchvision==0.9.1 numpy==1.20.2 opencv-python==4.5.1

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⚙️ Configuration

Before running training or inference, configure parameters in the conf.py file. This includes:

• Model settings
• Modality control
• Missing data simulation options
• Training hyperparameters

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🏃‍♀️ Training

To start training (using 2 GPUs), simply run:

bash run.sh

This internally calls:

python -m torch.distributed.launch --nproc_per_node=2 --master_port=21676 --use_env main.py ...

All training options (e.g., margin mode, prototype settings, seed) can be modified directly in run.sh.

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🔍 Inference

To run inference on test data:

python local_test.py

Make sure that the paths and inference parameters are properly set in conf.py.

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🙏 Acknowledgements

This project partially builds upon the Deformable ProtoPNet framework. We thank the authors for their valuable open-source contributions, which helped inspire the development of our model.

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