Compact vision models match domain-specific foundation models for several retinal imaging classification tasks: A systematic benchmark

Dávid Isztl, Tahm Spitznagel, Gábor Márk Somfai and Rui Santos

PLOS ONE, 2026, vol. 21, issue 8, 1-12

Abstract: Large domain-specific foundation models have been widely adopted for retinal image analysis, yet systematic evidence for their advantage over compact general-purpose architectures remains scarce. We benchmarked nine model configurations spanning 22.8M to 303M parameters (vision transformers, hierarchical Swin Transformers, ConvNeXt, and the domain-specific RETFound models) across four tasks: OCT 8-class disease classification, and three fundus photography tasks (DME severity, glaucoma detection, and DR severity grading). All models were evaluated under identical training conditions, with both pretrained (on natural-domain image datasets) and from-scratch initializations compared using Mann-Whitney U tests. Pretraining improved accuracy by 5.18–18.41 percentage points across all tasks (p

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0356202

DOI: 10.1371/journal.pone.0356202

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