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Vision Transformers and Hybrid Models Lead in Retinal Disease Screening Benchmarks

By PulseAugur Editorial · [1 sources] ·

Researchers have benchmarked various deep learning architectures, including convolutional neural networks, vision transformers, hybrid models, and vision-language models, for multi-disease retinal screening. The study utilized the Retinal Fundus Multi-disease Image Dataset (RFMiD) and evaluated performance on binary screening and multi-label classification tasks. Attention-based models, specifically SwinTiny and hybrid architectures like CoAtNet0 and MaxViTTiny, demonstrated superior performance in both binary and multi-label settings. Vision-language models were competitive but did not outperform the best transformer and hybrid backbones. The findings offer a reproducible reference for selecting models for automated retinal screening tools intended for clinical deployment. AI

IMPACT Hybrid and transformer models show promise for improving automated retinal disease screening accuracy.

RANK_REASON The cluster contains an academic paper detailing research findings and benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vision Transformers and Hybrid Models Lead in Retinal Disease Screening Benchmarks

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The cluster contains an academic paper detailing research findings and benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Durjoy Dey, Aymane Ajbar, Yuhong Yan ·

    Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening

    arXiv:2605.26283v1 Announce Type: cross Abstract: Modern deep learning offers powerful tools for automated retinal screening, but it remains unclear how different visual model families compare in realistic multi-disease settings and under domain shift. In this work, we benchmark …

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