Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

ABSTRACT: Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye. In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-tra

SUBMITTER: Sukei E 

PROVIDER: S-EPMC11538269 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature