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使用多模式MRI数据对主要抑郁症进行分类:个性化联合算法

Zhipeng Fan¹, Jingrui Xu¹, Jianpo Su¹

¹College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

|October 29, 2025

概括

联合学习使大脑成像模型在多个机构之间进行重大抑郁症 (MDD) 诊断的协作训练,而无需共享敏感的MRI数据. 该pF-GMCO算法实现了79.07%的准确性,提供了一个保护隐私的诊断框架.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
医疗成像医学成像

背景情况:

重度抑郁症 (MDD) 的准确诊断依赖于神经成像,但多站点数据存在异质性和隐私挑战.
由于所有权,安全和隐私问题,共享原始MRI数据受到限制,阻碍了强大的诊断模型开发.
联合学习 (FL) 提供了一个保护隐私的方法,用于跨站点的协作模式培训,而无需共享原始数据.

研究的目的:

开发一个以隐私为基础的联合学习框架,用于使用多式核磁共振 (MRI) 进行可扩展的多站点诊断主要抑郁障碍 (MDD).
为了解决多站点神经成像数据中固有的领域转移问题.
加强结构性MRI (sMRI) 和功能性MRI (fMRI) 的整合,以改善MDD的分类.

主要方法:

提出了个性化的联邦梯度匹配和对比优化 (pF-GMCO) 算法.
嵌入式梯度匹配与共弦相似性用于适应性站点贡献权重.
利用对比式学习进行客户端特定模型优化和多式联网紧双线 (MCB) 聚合以实现功能集成.

主要成果:

在Rest-Meta-MDD数据集上评估了pF-GMCO,包括23个地点的2293名受试者.
在重大抑郁障碍 (MDD) 中,诊断准确率达到了79.07%.
与现有方法相比,表现出卓越的性能和可解释性.

结论:

pF-GMCO提供了一种有效且对隐私有意识的框架,用于使用联合学习进行多站点MDD诊断.
该方法成功地解决了域转移问题,并集成了多模式MRI数据.
这种方法促进了心理健康障碍的协作研究和诊断工具的开发.

关键词:

梯度匹配的匹配方式大型抑郁症主要是抑郁症.模型对比的优化优化多模式MRI数据个性化的联合学习.

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