TMI20: Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
Pytorch implementation
Paper
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis.
IEEE Transactions on Medical Imaging, 2020
Installation
- Install Python 3.7.4, Pytorch 1.1.0, torchvision 0.3.0 and CUDA 9.0
- Or Check requirements.txt
- Clone this repo
git clone https://github.com/xmengli999/self_supervised
cd self_supervised
Data Preparation
- Download Ichallenge-AMD dataset,
synthesized FFA and
file_index
- Put them under
./data/
Evaluate
- Download our models, password: gja3, and put it under
./savedmodels/
- cd
scripts
- Run
sh evaluate_fold.sh
to start the evaluation process - 5-fold cross-validation results:
AUC | Accuracy | Precision |
---|---|---|
74.58% | 86.58% | 83.2% |
Train
- cd
scripts
- Run
sh train_fold.sh
to start the training process - See
train_ablation.sh
for ablation study - See
supervised_fundus.py
for supervised baselines
Note
- Contact: Xiaomeng Li (xmengli999@gmail.com)
Citation
If this code is useful for your research, please consider citing:
@article{li2020self,
title={Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis},
author={Li, Xiaomeng and Jia, Mengyu and Islam, Md Tauhidul and Yu, Lequan and Xing, Lei},
journal={IEEE Transactions on Medical Imaging},
year={2020},
publisher={IEEE}
}