用联邦学习提升糖尿病视网膜病变诊断准确率，保护隐私且适配资源匮乏地区。
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions
- 采用EfficientNetB0架构，通过多机构联邦学习整合眼底图像数据。
- 在未见数据集上达到93.21%准确率，低质量图像上仍保持91.05%性能。
- 模型已部署至两款应用，适合基层医疗快速诊断使用。
糖尿病视网膜病变是全球工作年龄人群失明的主要原因，但资源匮乏地区缺乏眼科医生。当前先进的深度学习系统因泛化能力差，在此类机构表现不佳。本文提出一种基于EfficientNetB0架构的新型联邦学习系统，利用多机构眼底图像数据，在保护患者隐私的前提下提升诊断泛化能力。该模型在未见数据集上的五分类任务中达到93.21%准确率，在模拟资源匮乏机构的低质量图像上仍保持91.05%准确率。模型已成功部署至两款移动应用，实现快速精准诊断。
原文摘要 · Abstract (English)
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.
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