FedOSS:通过客户间差异和协作进行联合开放集识别.
IEEE transactions on medical imaging
|July 10, 2023
概括
联合开放集识别 (FedOSR) 通过跨站点训练模型来解决医疗AI中的隐私风险. 一个新的框架,FedOSS,合成未知疾病样本,以提高已知和未见疾病的准确性.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 医学上的开放式识别 (OSR) 旨在对已知的疾病进行分类,并识别未知的疾病.
- 由于集中式数据聚合,传统的OSR面临隐私和安全风险.
- 联合学习 (FL) 为分布式医疗数据提供了一个保护隐私的解决方案.
研究的目的:
- 引入联邦开放集识别 (FedOSR) 作为医疗AI的新方法.
- 提出联邦开放集合成 (FedOSS) 框架,以应对佛罗里达州无法使用的未知样本的挑战.
- 增强医疗人工智能的能力,以区分已知的疾病和新的,未见的条件.
主要方法:
- 为FedOSR开发了联合开放集合成 (FedOSS) 框架.
- 引入了离散未知样本合成 (DUSS),使用客户间知识生成虚拟未知样本.
- 实施联邦开放空间采样 (FOSS) 来估计开放数据空间分布,并改善样本多样性.
主要成果:
- 实际上,FedOSS有效地产生了对学习决策边界至关重要的虚拟未知样本.
- 通过废弃研究,DUSS和FOSS模块通过废弃研究证明了对框架性能的重大贡献.
- 与医疗数据集上现有的最先进方法相比,FedOSS框架实现了更高的性能.
结论:
- 在医疗AI中,FedOSS提供了一种有效的解决方案,用于保护隐私的开放式集识别.
- 该框架成功地解决了在联合学习环境中未知样本稀缺性的挑战.
- 这项工作在将联合学习应用于复杂的医疗诊断任务方面取得了重大进展.
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