继发式学习:用于临床多站点深度学习的物理安全框架
Zi-Hao Bo1,2, Yuchen Guo3, Jinhao Lyu4
1School of Software, Tsinghua University, Beijing, China.
NPJ digital medicine
|November 5, 2023
概括
继电学习是一种新的深度学习框架,可以在不影响患者隐私的情况下进行安全的多站点医疗数据分析. 这种方法可以显著提高各种医疗任务的诊断准确性,同时保持数据主权.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗保健中的深度学习
- 数据安全与隐私数据安全与隐私
背景情况:
- 多站点临床数据对于医疗保健中强大的深度学习模型至关重要.
- 数据共享受到重大安全和隐私问题的阻碍.
- 目前的多站点学习方法在很大程度上依赖于网络安全基础设施.
研究的目的:
- 引入Relay Learning,一个安全的深度学习框架,用于多站点临床数据分析.
- 在协作医疗AI中解决数据安全和隐私挑战.
- 为了利用多站点大数据,同时确保本地数据的隔离.
主要方法:
- 继电学习框架的开发,该框架物理隔离临床数据.
- 在三个不同的医疗任务中证明有效性:视网膜底部细分,中瘤诊断和大脑中线局部化.
- 通过多站点和外部验证的评估,使用来自21个主机的41,038张医学图像.
主要成果:
- 在使用继电学习的三个医疗任务中观察到显著的性能改善.
- 平均性能提高了44.4% (视网膜细分),24.2% (瘤诊断) 和36.7% (大脑定位).
- 继电学习在外部测试集上表现优于中央学习,保持本地数据主权.
结论:
- 继电学习为医疗保健中的多站点深度学习提供了安全有效的解决方案.
- 该框架增强了合作,而不需要跨站点网络数据传输.
- 继电学习有可能改变临床多站点协作和推进医疗保健.
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