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为阿尔茨海默病的检测和检测提供安全的联合学习

Angela Mitrovska^1,2, Pooyan Safari¹, Kerstin Ritter^2,3

¹Fraunhofer-Institut fur Nachrichtentechnik, Heinrich-Hertz-Institute (HHI), Berlin, Germany.

Frontiers in aging neuroscience

|March 22, 2024

概括

联合学习 (FL) 允许使用脑MRI扫描检测阿尔茨海默病 (AD),而无需共享私人患者数据. 这种方法保持了隐私,并且与集中式方法相比,即使数据多样化,效果也相当.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

机器学习 (ML) 对神经成像分析具有前景,但受到数据隐私问题和缺乏大型公共数据集的阻碍.
联邦学习 (FL) 通过在不影响患者隐私的情况下实现对分散数据的模型培训来提供解决方案.

研究的目的:

在联邦学习 (FL) 框架内使用结构MRI (sMRI) 数据训练用于阿尔茨海默病 (AD) 检测的机器学习 (ML) 模型.
为了比较FL算法 (联合平均和安全聚合) 与集中式ML训练的性能.
调查数据异质性 (人口统计,诊断,失衡) 对FL模型在AD检测中的性能的影响.

主要方法:

实施了两个FL聚合算法:联合平均 (FedAvg) 和安全聚合 (SecAgg).
模拟异质环境,以评估人口和诊断数据分布的影响.
将FL模型与在相同数据上训练的集中式ML模型进行比较.
使用模拟的会员推断攻击对隐私保证的评估.

主要成果:

FL模型的表现与阿尔茨海默病检测集中训练的表现相当.
模拟的异质环境揭示了数据分布差异对FL模型准确性的影响.
与FedAvg和集中式方法相比,安全聚合 (SecAgg) 提供了针对会员推断攻击的增强隐私保护.

结论:

联合学习 (FL) 是一种可行且保护隐私的方法,用于从神经成像数据中开发用于阿尔茨海默病检测的ML模型.
解决数据异质性对于在临床应用中强大的FL模型开发至关重要.
FL,特别是安全聚合 (SecAgg),在医疗AI研究中在保护患者隐私方面提供了显著的优势.

关键词:

阿尔茨海默病阿尔茨海默病联邦学习学习 (Federated Learning) 是一种学习方式.机器学习机器学习安全聚合安全聚合安全的多方计算安全多方计算神经成像是一种神经成像.

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