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

Author

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  • Dávid Isztl
  • Tahm Spitznagel
  • Gábor Márk Somfai
  • Rui Santos

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

Suggested Citation

  • Dávid Isztl & Tahm Spitznagel & Gábor Márk Somfai & Rui Santos, 2026. "Compact vision models match domain-specific foundation models for several retinal imaging classification tasks: A systematic benchmark," PLOS ONE, Public Library of Science, vol. 21(8), pages 1-12, August.
    • Handle: RePEc:plo:pone00:0356202
    DOI: 10.1371/journal.pone.0356202
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