标题： 联邦学习在糖尿病视网膜病变诊断中的应用：增强欠资源地区的准确性和普适性 显示英文标题

标题： Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions

摘要： 糖尿病视网膜病变是全球范围内劳动年龄成人视力丧失的主要原因，然而资源不足的地区缺乏眼科医生。目前最先进的深度学习系统由于泛化能力有限，在这些机构中表现不佳。本文探索了一种新颖的联邦学习系统，用于糖尿病视网膜病变诊断，采用EfficientNetB0架构，以利用多个机构的眼底数据，提高资源不足医院的诊断泛化能力，同时保护患者隐私。联邦模型在一个未见过的数据集上五分类准确率达到93.21%，在模拟资源不足机构的低质量图像上达到91.05%。该模型被部署到两个应用程序中，以便快速且准确地进行诊断。 显示英文摘要

摘要： Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability. This paper explores a novel federated learning system for diabetic retinopathy diagnosis with the EfficientNetB0 architecture to leverage fundus data from multiple institutions to improve diagnostic generalizability at under-resourced hospitals while preserving patient-privacy. The federated model achieved 93.21% accuracy in five-category classification on an unseen dataset and 91.05% on lower-quality images from a simulated under-resourced institution. The model was deployed onto two apps for quick and accurate diagnosis.