公平的多模式正规相关性分析:对阿尔茨海默病的神经成像研究
Zhuoping Zhou1, Boning Tong1, Bojian Hou1
1University of Pennsylvania, Philadelphia, PA, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
本研究引入了公平的多模体法典相关性分析 (F-MCCA),以减少多模体数据分析中的偏差. F-MCCA优化了相关性和人口公平性,确保了敏感群体之间公平的见解.
科学领域:
- 多模式数据分析多模式数据分析
- 统计学学习 统计学学习
- 机器学习的公平性
背景情况:
- 多模态法定关联分析 (MCCA) 用于分析跨数据集的关系.
- 在标准MCCA.中存在对人口偏差的担忧.
- 人工智能和统计方法的公平性对于公平的应用至关重要.
研究的目的:
- 引入公平的多模式法典分析 (F-MCCA),以解决MCCA中的公平性问题.
- 开发一种优化相关性表现和人口公平性的方法.
- 减轻多模式数据分析中的偏见,特别是在医疗保健领域.
主要方法:
- 使用多目标优化框架开发了公平的MCCA (F-MCCA).
- 使用相关差异错误 (CDE) 量化差异.
- 为了在敏感群体之间保持一致的相关性,推导出投影矩阵.
主要成果:
- F-MCCA在公平度指标方面取得了实质性的改进,在相关性表现方面做出了最小的牺牲.
- 在使用性作为敏感属性的阿尔茨海默病神经成像计划的神经成像数据上进行了验证.
- 下游分类任务显示,人口统计学平价降低,赔率差异均等.
结论:
- F-MCCA有效地平衡了分析性能与公平性考虑.
- 该方法支持医疗保健中更加公正的多模式数据分析.
- F-MCCA提供了一个有前途的方法,可以从复杂的数据集中获得公平的见解.
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