IEEE TMI21: Rotation-oriented Collaborative Self-supervised Learning for Retinal Disease Diagnosis

Pytorch implementation

Paper

IEEE TMI21: [Rotation-oriented Collaborative Self-supervised Learning for Retinal Disease Diagnosis.]

Installation

• Install Python 3.7.4, Pytorch 1.1.0, torchvision 0.3.0 and CUDA 8.0
• Or Check requirements.txt
• Clone this repo

git clone https://github.com/xmengli999/Rotation-oriented-self-supervised
cd Rotation-oriented-self-supervised

Data Preparation

• Download Ichallenge-AMD dataset, file_index
  
• Put them under ./data/
• The folder should be

./data/Training400/resized_image_320/XXX.jpg

./data/Training400/random_list.txt

Evaluate

• Download our models, password: h7z6, and put it under ./savemodels/
• cd scripts
• Run scripts in eval_fold.sh to start the evaluation process
• 5-fold cross-validation results (Table I in the paper):

AUC	Accuracy	Precision
75.64%	87.09%	83.96%

• Download our models, password: 2juk, and put it under ./savemodels/
• train on DR, test on AMD (Table II in the paper) -- this step requires Pytorch 1.6.0:

AUC	Accuracy	Precision
78.11%	87.85%	85.58%

Train

• cd scripts
• Check scripts in train_fold.sh to start the training process

Citation

If this code is useful for your research, please consider citing:

@ARTICLE{9411868,
author={Li, Xiaomeng and Hu, Xiaowei and Qi, Xiaojuan and Yu, Lequan and Zhao, Wei and Heng, Pheng-Ann and Xing, Lei},
journal={IEEE Transactions on Medical Imaging}, 
title={Rotation-oriented Collaborative Self-supervised Learning for Retinal Disease Diagnosis}, 
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TMI.2021.3075244}}

Note

• Contact: Xiaomeng Li ([email protected])