卷积、Transformer、混合模型及视觉语言模型在多病种视网膜筛查中的基准测试
Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
AI总结：
本研究在RFMiD数据集上对四种模型家族的12种架构进行基准测试，评估其在多病种视网膜筛查中的性能，发现基于注意力的模型（如SwinTiny、CoAtNet0、MaxViTTiny）在二元筛查和多标签分类中表现最佳，视觉语言模型与CNN基线相当但未超越最优Transformer和混合模型。
摘要：
现代深度学习为自动化视网膜筛查提供了强大工具，但在现实多病种设置和领域偏移下，不同视觉模型家族的比较仍不明确。本研究使用视网膜眼底多病种图像数据集（RFMiD），对四种模型家族（卷积神经网络、视觉Transformer、混合CNN-Transformer骨干网络和视觉语言模型）的12种架构进行基准测试。我们评估两个任务：任何视网膜疾病的二元筛查和28个疾病类别的多标签分类。通过标准化训练、校准和评估协议，我们报告了在特异性接近80%的临床相关操作点下的AUC、F1、精确率、召回率和灵敏度。在RFMiD上，所有架构在二元筛查中表现良好，AUC均高于84%，但基于注意力的模型表现最佳。SwinTiny以及混合模型CoAtNet0和MaxViTTiny在二元筛查中取得最强结果，并在多标签设置中提高了宏F1和微F1。视觉语言模型（包括CLIP ViT-B/16和SigLIP-Base384）与CNN基线相当，但未超越最优Transformer和混合骨干网络。在Messidor-2上对可转诊糖尿病视网膜病变进行外部验证时，AUC范围为66.8%至84.7%，混合模型和Transformer模型再次表现出强劲性能。这些结果为多病种视网膜筛查中的模型选择提供了可重复的参考，并指导未来用于临床部署的自动化筛查工具。
英文摘要：
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 twelve architectures across four model families: convolutional neural networks, vision transformers, hybrid CNN-transformer backbones, and vision-language models, using the Retinal Fundus Multi-disease Image Dataset (RFMiD). We evaluate two tasks: binary screening for any retinal disease and multi-label classification across 28 disease classes. Using standardized training, calibration, and evaluation protocols, we report AUC, F1, precision, recall, and sensitivity at a clinically relevant operating point with specificity near 80%. On RFMiD, all architectures perform well on binary screening, with AUC above 84%, but attention-based models perform best. SwinTiny and the hybrid CoAtNet0 and MaxViTTiny models achieve the strongest binary screening results and improve macro and micro F1 in the multi-label setting. Vision-language models, including CLIP ViT-B/16 and SigLIP-Base384, are competitive with CNN baselines but do not surpass the best transformer and hybrid backbones. In external validation on Messidor-2 for referable diabetic retinopathy, AUC ranges from 66.8% to 84.7%, with hybrid and transformer models again showing strong performance. These results provide a reproducible reference for model selection in multi-disease retinal screening and guide future automated screening tools for clinical deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
