异步和焦点联合学习用于根据当地数据稀缺性和类不平衡的皮肤病变分类
Shichao Ma1, Yun-Hin Chan2, Edith C H Ngai2
1School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D(2)4H), Hong Kong Science Park, Hong Kong SAR, China.
Computer methods and programs in biomedicine
|September 13, 2025
概括
联合学习 (FL) 在远程医疗中常见的有限,不平衡的数据中扎. 我们的新异步,焦点更新方法在这些具有挑战性的医疗保健环境中显著提高了皮肤病变分类的准确性.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 联合学习 (FL) 在分散的数据节点上训练模型,增强医疗保健应用程序的隐私.
- 传统的FL方法假设每个节点有大量的数据,这不适合新兴的远程医疗和医疗物联网 (IoMT) 场景.
- 像IoMT和远程医疗这样的医疗保健环境面临着许多节点的挑战,这些节点具有稀缺,不平衡的数据集.
研究的目的:
- 开发和评估一个改进的联合学习 (FL) 方法,用于医疗保健应用程序,数据稀缺和阶级不平衡.
- 解决分布式网络中的标准FL方法的局限性,这些分布式网络具有小的,非独立的数据贡献.
主要方法:
- 为联合学习 (FL) 提出了一个异步和焦点更新方法.
- 该方法应用于远程皮肤病理学用例,培训皮肤病变图像分类器.
- 在FL框架中使用了来自798个移动设备的1,268张皮肤病变图像的数据集.
主要成果:
- 标准的FL算法 (FedAvg,FedProx,FedNova) 在数据稀缺和不平衡的情况下表现不佳 (AUROC 0.57-0.67).
- 提出的异步和焦点FL方法实现了明显更高的测试AUROC,为0.78-0.89.
- 新型FL方法的性能接近通过集中培训获得的0.91的最佳AUROC.
结论:
- 开发的异步和焦点FL方法有效地解决了现实的远程医疗和IoMT应用中的数据稀缺性和类不平衡问题.
- 这种方法为在分布式医疗保健网络中高效地实施FL提供了可行的解决方案.
- 这些发现突显了定制的FL策略在数据受限的医疗环境中改善ML模型性能的潜力.
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