Distributed training of foundation models for ophthalmic diagnosis.

ABSTRACT: Vision impairment affects nearly 2.2 billion people globally, and nearly half of these cases could be prevented with early diagnosis and intervention-underscoring the urgent need for reliable and scalable detection methods for conditions like diabetic retinopathy and age-related macular degeneration. Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. We employed a self-supervised, mask-based pre-training strategy to develop a robust foundation encoder. This encoder was trained on seven optical coherence tomography datasets, and we compared its performance under local, centralized, and federated learning settings. Our results show that

SUBMITTER: Gholami S 

PROVIDER: S-EPMC11754456 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature