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通过深度神经网络进行联合学习:一种保护隐私的方法来增强心电图分类.

Kuba Weimann, Tim O F Conrad

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    此摘要是机器生成的。

    联合学习用于从心电图 (ECG) 数据中诊断心脏异常,可以在不共享患者信息的情况下进行协作. 这种保护隐私的方法优于孤立培训,几乎与集中式方法相匹配.

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    科学领域:

    • 人工智能在医学中的应用
    • 心脏病学 心脏病学
    • 机器学习用于医疗保健

    背景情况:

    • 越来越多的数据隐私法规需要新的医疗数据分析方法.
    • 从心电图 (ECG) 数据中诊断心脏异常需要强大且保护隐私的机器学习模型.
    • 传统的集中式培训模式需要共享敏感的患者数据,这会给隐私带来风险.

    研究的目的:

    • 评估联合学习 (FL) 在诊断心脏异常的有效性,使用心电图数据的深度残留网络.
    • 将FL的表现与集中培训 (与数据共享) 和孤立培训场景进行比较.
    • 评估FL模型在不同,未见的患者数据中的概括性.

    主要方法:

    • 利用了来自PhysioNet/Computing in Cardiology Challenge 2021的公开可用的心电图数据.
    • 实施并比较了深度残余网络的三个联合学习算法.
    • 与集中培训和孤立的本地培训模式对比,对FL的绩效进行了基准测试.

    主要成果:

    • 联合学习显著优于单独训练的心电图分类器.
    • 全球训练的FL模型,当在本地微调时,超过了非协作方法.
    • 在非分销数据上,FL取得了与集中培训相美的表现,表现出强烈的概括性.

    结论:

    • 联合学习为医疗保健中的协作心电图分析提供了一个可行的,保护隐私的解决方案.
    • FL模型学习可概括的心脏特征,这些特征可以适应特定的机构数据集.
    • 这种方法有效地解决了数据隐私问题,同时实现了强大的心脏异常诊断.