GONet通过多源域训练提升青光眼检测泛化能力，跨人群表现更优。
GONet: A Generalizable Deep Learning Model for Glaucoma Detection
- 基于DINOv2自监督视觉变换器，采用多源域策略微调
- 在目标领域AUC达0.85-0.99，比杯盘比高21.6%
- 适用于跨种族、跨设备的青光眼筛查，适合临床部署
青光眼视神经病变（GON）是常见致盲眼病，早期发现至关重要。传统诊断依赖多种眼科检查，耗时且需专科医生。现有深度学习模型虽有潜力，但跨人群、跨设备泛化能力有限。为此，我们提出GONet，基于7个独立数据集构建，包含超11.9万张带金标准标注的数字眼底图像，覆盖多样地理背景。GONet采用DINOv2预训练自监督视觉变换器，并通过多源域策略微调。其在目标域表现出强泛化性，AUC为0.85–0.99，性能与或优于现有最优方法，显著优于杯盘比，最高提升21.6%。模型已公开，同时发布一个包含768张带标签眼底图的新开放数据集。
原文摘要 · Abstract (English)
Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The traditional diagnostic approach for GON involves a set of ophthalmic examinations, which are time-consuming and require a visit to an ophthalmologist. Recent deep learning models for automating GON detection from digital fundus images (DFI) have shown promise but often suffer from limited generalizability across different ethnicities, disease groups and examination settings. To address these limitations, we introduce GONet, a robust deep learning model developed using seven independent datasets, including over 119,000 DFIs with gold-standard annotations and from patients of diverse geographic backgrounds. GONet consists of a DINOv2 pre-trained self-supervised vision transformers fine-tuned using a multisource domain strategy. GONet demonstrated high out-of-distribution generalizability, with an AUC of 0.85-0.99 in target domains. GONet performance was similar or superior to state-of-the-art works and was significantly superior to the cup-to-disc ratio, by up to 21.6%. GONet is available at [URL provided on publication]. We also contribute a new dataset consisting of 768 DFI with GON labels as open access.
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