This is the repo for the paper RETFound: a foundation model for generalizable disease detection from retinal images led by Yukun Zhou, Daniel C. Alexander, and Pearse A. Keane:
- RETFound is pre-trained on 1.6 million retinal images with self-supervised learning
- RETFound has been validated in multiple disease detection tasks
- RETFound can be efficiently adapted to customised tasks
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.
Main Requirements
torch==1.8.1
pandas==0.25.3
timm==0.3.2
tqdm
Installation
conda create -n retfound python=3.7.5 -y
conda activate retfound
cd RETFound_MAE
pip install -r requirement.txtDownload Model
| ViT-Large | |
|---|---|
| Colour fundus image | download |
| OCT | download |
Data Preparation
Organise your data into this directory structure (using IDRiD as an example)
Training
python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_finetune.py \
--batch_size 16 \
--world_size 1 \
--model vit_large_patch16 \
--epochs 50 \
--blr 5e-3 --layer_decay 0.65 \
--weight_decay 0.05 --drop_path 0.2 \
--nb_classes 5 \
--data_path ./IDRiD_data/ \
--task ./finetune_IDRiD/ \
--finetune ./RETFound_cfp_weights.pthTesting
python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_finetune.py \
--eval --batch_size 16 \
--world_size 1 \
--model vit_large_patch16 \
--epochs 50 \
--blr 5e-3 --layer_decay 0.65 \
--weight_decay 0.05 --drop_path 0.2 \
--nb_classes 5 \
--data_path ./IDRiD_data/ \
--task ./internal_IDRiD/ \
--resume ./finetune_IDRiD/checkpoint-best.pthPlease contact ykzhoua@gmail.com or yukun.zhou.19@ucl.ac.uk if you have questions.
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.
The code is based on MAE. We thank the authors for releasing the code. We also appreciate the data organisers for sharing their data.
If you find this repository useful, please consider citing this paper:
@article{zhou2023foundation,
title={A foundation model for generalizable disease detection from retinal images},
author={Zhou, Yukun and Chia, Mark A and Wagner, Siegfried K and Ayhan, Murat S and Williamson, Dominic J and Struyven, Robbert R and Liu, Timing and Xu, Moucheng and Lozano, Mateo G and Woodward-Court, Peter and others},
journal={Nature},
pages={1--8},
year={2023},
publisher={Nature Publishing Group UK London}
}