医疗联合模型与个性化和共享组件的混合
IEEE transactions on pattern analysis and machine intelligence
|September 27, 2024
概括
联合学习提供保护隐私的医疗人工智能,但与各种数据作斗争. 本研究介绍了一个个性化的框架,以提高模型性能和通信效率,以实现安全,协作式的医疗分析.
科学领域:
- 医疗人工智能 医疗人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 医疗保健中的数据驱动方法引发了隐私问题.
- 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训,增强隐私.
- 各机构的异质医疗数据降低了FL的表现.
研究的目的:
- 提出一个个性化的联合学习框架,以异构的医疗数据解决绩效退化问题.
- 提高医疗应用联合学习中的通信和计算效率.
主要方法:
- 开发了一个个性化的联合学习框架,考虑了本地数据相似性.
- 引入了差异稀疏调节器,以提高通信效率.
- 实施的方法,以减少计算成本在联邦模型培训期间.
主要成果:
- 拟议的框架实现了卓越的个性化模型,平衡了概括和个性化.
- 沟通效率显著提高 (高达60%).
- 在五个现实数据集上,在结节分类,瘤细分和临床风险预测任务中表现优于现有的14种方法.
结论:
- 个性化的联合学习框架有效地处理异构的医疗数据.
- 这项研究在高效的私人协作医疗AI方面取得了重大进展.
- 提出的方法为需要隐私和性能的现实世界临床应用提供了强大的解决方案.
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