CLAT

Official implementation of CLAT: Concept-based Lesion Aware Transformer for Interpretable Retinal Disease Diagnosis (TMI, 2024)

Usage

Installation

Recommended environment:

• python 3.9.7
• pytorch 2.0.1
• torchvision 0.15.2
• lightning 2.1.0

To install the dependencies, run:

git clone https://github.com/Sorades/CLAT.git
cd CLAT
pip install -r requirements.txt

Dataset

• FGADR Dataset
• DDR Dataset

The annotation files are placed at ./data

Training and Testing

Modify the settings in ./config/default.yaml, and then run the commands below to train and test the model:

python main.py fit_and_test --config configs/default.yaml --data configs/data/FGADDR.yaml

# test with automatic intervention
python main.py exp_int --config configs/default.yaml --data configs/data/FGADDR.yaml

Citation

@article{wen2024concept,
  title={Concept-based Lesion Aware Transformer for Interpretable Retinal Disease Diagnosis},
  author={Wen, Chi and Ye, Mang and Li, He and Chen, Ting and Xiao, Xuan},
  journal={IEEE Transactions on Medical Imaging},
  year={2024},
  publisher={IEEE},
  doi={10.1109/TMI.2024.3429148}
}