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集群MFL:一个集群增强的模式不完整的多模式联合学习在脑成像分析的框架

Xinpeng Wang, Rong Zhou, Han Xie

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

本研究介绍了ClusMFL,这是一个新的多式联网学习 (MFL) 框架,解决了缺失的脑成像数据. 通过使用特征集群和模式意识策略来提高绩效,ClusMFL增强了跨机构的分析.

科学领域:

人工智能的人工智能
医疗成像医学成像
机器学习机器学习

背景情况:

多模式联合学习 (MFL) 显示出在医疗保健领域合作模式培训的前景.
模式不完整,缺少特定的大脑成像数据 (PET,MRI,CT) 是现实应用中的一个重大挑战.
现有的MFL方法经常假定完整的数据或过度简化缺失模式的场景.

研究的目的:

提出ClusMFL,一个新的MFL框架,旨在在现实模式不完整的情况下进行跨机构脑成像分析.
解决客户级和实例级的模式不完整问题.
为了实现有效的知识转移和模型培训,即使缺少数据模式.

主要方法:

集群MFL利用功能集群使用FINCH算法创建模式标签特定的集群中心.
监督对比学习用于在模式内对特征进行调整.
集群中心作为缺失模式的代理,促进跨模式的知识转移.
使用一种模式意识的聚合策略,在严重不完整的数据场景中提高性能.

主要成果:

在ADNI数据集上使用结构MRI和PET扫描来评估ClusMFL.
该框架在不同级别的模式不完整性方面,与基线方法相比,展示了最先进的性能.
结果显示ClusMFL在处理缺失数据和改进跨机构脑成像分析方面的有效性.

结论:

在脑成像分析中,ClusMFL提供了一个可扩展和有效的解决方案,用于多模式联合学习,特别是在模式不完整的情况下.
拟议的特征集群和模式意识聚合策略显著改善了在具有挑战性的现实场景中模型的性能.
该框架通过强大处理缺失数据,推进了协作医疗图像分析.

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