bibtext: @InProceedings{ Du_RETCLIP_MICCAI2024,
                   author = { Du, Jiawei and Guo, Jia and Zhang, Weihang and Yang, Shengzhu and Liu, Hanruo and Li, Huiqi and Wang, Ningli },
                   title = { { RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: The Vision-Language Foundation model is increasingly investigated in the fields of computer vision and natural language processing, yet its exploration in ophthalmology and broader medical applications remains limited. The challenge is the lack of labeled data for the training of foundation model. To handle this issue, a CLIP-style retinal image foundation model is developed in this paper. Our foundation model, RET-CLIP, is specifically trained on a dataset of 193,865 patients to extract general features of color fundus photographs (CFPs), employing a tripartite optimization strategy to focus on left eye, right eye, and patient level to reflect real-world clinical scenarios. Extensive experiments demonstrate that RET-CLIP outperforms existing benchmarks across eight diverse datasets spanning four critical diagnostic categories: diabetic retinopathy, glaucoma, multiple disease diagnosis, and multi-label classification of multiple diseases, which demonstrate the performance and generality of our foundation model. We will release our pre-trained model publicly in support of further research.
title: RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
authors: Du, Jiawei Guo, Jia Zhang, Weihang Yang, Shengzhu Liu, Hanruo Li, Huiqi Wang, Ningli