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.

本研究解决了眼科影像中基础模型预训练在提高年龄相关性黄斑变性（AMD）识别能力上的不确定性。通过对六种自监督学习预训练的视觉变换器（ViTs）进行基准测试，发现基于自然图像预训练的iBOT在外部分布泛化上表现最佳，AUROC值高达0.80-0.97。这些发现不仅突显了基础模型在改善AMD识别中的价值，还挑战了"in-domain"预训练必要性的假设。

基于基准测试的眼科基础模型用于临床显著年龄相关性黄斑变性检测