基于数据子集表示的垂直联合学习用于医疗保健应用程序.
Yukun Shi1, Jilin Zhang1, Meiting Xue1
1School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
Computer methods and programs in biomedicine
|February 15, 2025
概括
通过使医院能够使用垂直联合学习 (VFL) 协同训练模型,FedRL改善了医疗保健AI. 这种方法在有限的数据基础上提高了疾病分类的性能,优于现有的方法.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于医学诊断
- 为数据隐私提供联合学习.
背景情况:
- 垂直联合学习 (VFL) 允许跨医院的协作模式培训,同时保持患者的隐私.
- 现有的VFL方法由于样本/标签有限以及缺乏跨医院数据相关性而难以获得医疗保健数据.
- 医疗保健人工智能需要强大的疾病分类和诊断模型.
研究的目的:
- 引入FedRL,一种基于代表性的新型VFL方法,用于改善医疗保健AI.
- 解决VFL在低样本,低标签医疗保健场景中的挑战.
- 为了提高模型性能,利用跨医院的内在数据连接.
主要方法:
- 在联邦代表预训练中,FedRL使用了对齐的数据.
- 该方法涉及将本地数据分割,利用子集关系,并使用自定义丢失函数.
- 一个跨医院的协作表示模型被训练,以捕获全球数据的潜在表示.
主要成果:
- 在三个医疗保健数据集上,FedRL表现出卓越的性能.
- 该方法在准确度方面取得了4.7%的平均改进,在AUC方面达到5.6%,在F1得分方面达到4.8%.
- 在有限的标记样本中,FedRL显示出强度和一致的性能.
结论:
- FedRL是一种有效的VFL方法,用于医疗数据分析.
- 拟议的方法增强了疾病分类和临床诊断.
- 在有限的数据的情况下,FedRL为医疗保健中的隐私保护人工智能提供了一个有希望的解决方案.
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