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arXiv · 2505.05291

Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection

Benjamin A. Cohen·Jonathan Fhima·Meishar Meisel·Baskin Meital·Luis Filipe Nakayama·Eran Berkowitz·Joachim A. Behar
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Abstract

Self-supervised learning (SSL) has enabled Vision Transformers (ViTs) to learn robust representations from large-scale natural image datasets, enhancing their generalization across domains. In retinal imaging, foundation models pretrained on either natural or ophthalmic data have shown promise, but the benefits of in-domain pretraining remain uncertain. To investigate this, we benchmark six SSL-pretrained ViTs on seven digital fundus image (DFI) datasets totaling 70,000 expert-annotated images for the task of moderate-to-late age-related macular degeneration (AMD) identification. Our results show that iBOT pretrained on natural images achieves the highest out-of-distribution generalization, with AUROCs of 0.80-0.97, outperforming domain-specific models, which achieved AUROCs of 0.78-0.96 and a baseline ViT-L with no pretraining, which achieved AUROCs of 0.68-0.91. These findings highlight the value of foundation models in improving AMD identification and challenge the assumption that in-domain pretraining is necessary. Furthermore, we release BRAMD, an open-access dataset (n=587) of DFIs with AMD labels from Brazil.

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Benjamin A. Cohen, Jonathan Fhima, Meishar Meisel, Baskin Meital, Luis Filipe Nakayama, Eran Berkowitz, Joachim A. Behar. 2025-05-22. Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection. https://arxiv.org/abs/2505.05291

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