提出公平CCA方法，让脑影像分析更公正
Fair CCA for Fair Representation Learning: An ADNI Study
- 设计新算法使特征与敏感属性无关
- 在真实阿尔茨海默病数据上提升分类公平性
- 适合医疗影像中需要去偏的公平学习场景
典型相关分析（CCA）用于发现多模态数据间的相关性并学习低维表示。随着公平性在机器学习中愈发重要，公平CCA受到关注。然而，以往方法常忽视对下游分类任务的影响，限制了实际应用。本文提出一种新的公平CCA方法，确保投影特征与敏感属性独立，从而在不损害准确性的前提下增强公平性。我们在合成数据和阿尔茨海默病神经影像计划（ADNI）的真实数据上验证该方法，结果表明其在保持高相关性分析性能的同时，显著提升了分类任务的公平性。本工作为神经影像研究中的无偏机器学习提供了可行方案。代码已开源：https://github.com/ZhanliangAaronWang/FR-CCA-ADNI。
原文摘要 · Abstract (English)
Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential. Code is available in https://github.com/ZhanliangAaronWang/FR-CCA-ADNI.
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