A foundation model for generalizable disease detection from retinal images.

ABSTRACT: 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 disorders¹. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications². 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 a

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC10550819 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature