Retina专用大模型在小数据系统病检测上优于传统模型。
Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
- 用retina特化大模型RETFound对比三种经典深度学习模型
- 小样本下RETFound在全身疾病检测上AUC显著更高
- 大数据时两者性能接近，适合不同数据规模场景
背景：自监督的视网膜专用基础模型RETFound在下游任务中展现出潜力，但其与传统深度学习（DL）模型的对比性能尚不明确。本研究评估了RETFound与三个ImageNet预训练的监督型DL模型（ResNet50、ViT-base、SwinV2）在眼病及全身性疾病检测中的表现。方法：在全量数据、50%、20%及固定样本量（400、200、100张图像，其中一半为病例；每类糖尿病视网膜病变严重程度分别有100和50例）下微调/训练模型。微调模型在内部测试集SEED（53,090张图）和APTOS-2019（3,672张图）上评估，并在基于人群的（BES、CIEMS、SP2、UKBB）及开源数据集（ODIR-5k、PAPILA、GAMMA、IDRiD、MESSIDOR-2）上进行外部验证。使用受试者工作特征曲线下面积（AUC）和经邦弗隆尼校正的Z检验（P<0.05/3）比较性能。结果：在大数据集下，传统DL模型与RETFound在眼病检测上基本相当；但在小数据集下，RETFound在全身性疾病检测上表现更优。结论：该研究揭示了传统模型与基础模型各自的优劣与局限性。
原文摘要 · Abstract (English)
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised DL models (ResNet50, ViT-base, SwinV2) in detecting ocular and systemic diseases. Methods: We fine-tuned/trained RETFound and three DL models on full datasets, 50%, 20%, and fixed sample sizes (400, 200, 100 images, with half comprising disease cases; for each DR severity class, 100 and 50 cases were used. Fine-tuned models were tested internally using the SEED (53,090 images) and APTOS-2019 (3,672 images) datasets and externally validated on population-based (BES, CIEMS, SP2, UKBB) and open-source datasets (ODIR-5k, PAPILA, GAMMA, IDRiD, MESSIDOR-2). Model performance was compared using area under the receiver operating characteristic curve (AUC) and Z-tests with Bonferroni correction (P<0.05/3). Interpretation: Traditional DL models are mostly comparable to RETFound for ocular disease detection with large datasets. However, RETFound is superior in systemic disease detection with smaller datasets. These findings offer valuable insights into the respective merits and limitation of traditional models and FMs.
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