维护隐私的联合学习,用于多机构环境中的协作医疗数据挖掘
Rahul Haripriya1, Nilay Khare2, Manish Pandey2
1Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, India. rahulharipriyamanit@gmail.com.
Scientific reports
|April 11, 2025
概括
这项研究结合了转移学习和联合学习,以实现安全的AI诊断. 它可以准确地对医疗图像进行分类,而不会损害敏感的患者数据.
科学领域:
- 医疗保健中的人工智能
- 医学成像分析 医学成像分析
- 数据隐私和安全数据隐私和安全
背景情况:
- 医疗诊断中越来越多地依赖人工智能,面临着关键的数据隐私挑战.
- 超过30%的医疗机构报告数据泄露,强调需要安全的AI解决方案.
- 现有的方法难以平衡模型准确性和患者数据保密性.
研究的目的:
- 调查转移学习和联合学习的整合,以保护隐私的医疗图像分类.
- 通过使用各种AI架构来评估拟议框架的通用性和稳定性.
- 引入和分析一种用于优化联合学习的新型自适应聚合方法.
主要方法:
- 使用了GoogLeNet和VGG16,在ImageNet进行了预训练,并对结核病X射线,脑瘤MRI和糖尿病视网膜病变数据集进行了微调.
- 员工转移学习与联合学习策略相结合.
- 开发和分析了一种基于数据分歧的新型自适应聚合方法,在联邦平均值 (FedAvg) 和联邦随机梯度下降 (FedSGD) 之间动态切换.
主要成果:
- 在使用基线和现代架构 (EfficientNetV2,ResNet-RS) 的多个医学成像数据集中实现了高分类准确性.
- 证明了拟议框架的可扩展性和稳定性.
- 适应性聚合方法优化了模型的融合,同时保持了数据隐私.
结论:
- 转移学习与联合学习相结合,为医学图像分类提供了可扩展,强大和安全的解决方案.
- 拟议的框架使医疗机构能够训练准确的AI诊断模型,而不影响患者数据隐私.
- 这种方法解决了对安全有效的人工智能驱动的医疗保健解决方案的关键需求.
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