分层边缘-云端架构实现高效糖尿病视网膜病变筛查
A Cascaded Edge-Cloud Architecture for Automated Diabetic Retinopathy Screening
- 边缘端用轻量模型初筛，云端仅处理高风险病例
- 降低50%以上云端调用次数，保持98.99%检出率
- 适合资源有限的基层医疗场景部署
糖尿病视网膜病变是导致可预防失明的主要原因，自动化筛查有助于缓解资源匮乏环境下的专业人力压力。本文在公开的APTOS 2019盲症检测数据集上评估两级边缘-云端级联架构：第一级在本地运行轻量级MobileNetV3-small模型，区分可转诊（2-4类）与不可转诊（0-1类）病例；第二级仅对第一级标记为可转诊的图像，在云端运行RETFound-DINOv2模型进行有序严重程度分级。在733张分层测试集上，第一级在高敏感性阈值下达到98.99%敏感度和84.37%特异度。部署导向的安全底限级联将49.52%的测试图像转发至云端，相较纯云端模型减少50.48%的图像调用。在4分类输出空间（0-1/2/3/4类）中，该级联方案获得77.49%准确率和0.7938的加权卡帕系数，而纯云端基线为80.76%准确率和0.8184卡帕系数。另一比较器虽提升至80.49%准确率和0.8167卡帕系数，但误漏12例真实可转诊病例。研究还报告了重复分层自助采样变异、依赖流行率的云端调用曲线、原始字节上传估算及本地层级的CPU延迟。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness, and automated screening can help extend specialist capacity in resource-constrained clinical workflows. Cloud-based deep learning systems can provide strong grading performance, but they require image upload and reliable connectivity. We evaluate a two-tier edge-cloud cascade on the public APTOS 2019 Blindness Detection dataset. Tier 1 runs a lightweight MobileNetV3-small model locally to triage Referable DR (Classes 2-4) versus Non-referable DR (Classes 0-1). Tier 2 runs a RETFound-DINOv2 model in the cloud for ordinal severity grading only on images flagged as referable by Tier 1. On a stratified APTOS test split of 733 images, Tier 1 reaches 98.99% sensitivity and 84.37% specificity at a validation-tuned high-sensitivity threshold. The deployment-oriented safety-floor cascade forwards 49.52% of test images to Tier 2, reducing image-count cloud calls by 50.48% relative to a cloud-only model. In the deployed 4-class output space (Class 0-1 / Class 2 / Class 3 / Class 4), the safety-floor cascade obtains 77.49% accuracy and 0.7938 quadratic weighted kappa, while the cloud-only baseline obtains 80.76% accuracy and 0.8184 kappa. A Tier-2-override comparator improves accuracy to 80.49% and kappa to 0.8167, but downgrades 12 true referable cases after Tier 1 correctly forwarded them. We also report repeated stratified bootstrap variability, a prevalence-dependent cloud-call curve, raw-byte upload estimates, and CPU latency for the local tier. Index Terms: Diabetic retinopathy screening, retinal image analysis, edge-cloud computing, cascaded inference, teleophthalmology, clinical decision support, medical artificial intelligence, resource-constrained healthcare
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