Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging.

翻译：缓解机器学习模型的歧视问题在医学图像分析领域日益受到关注。然而，针对具有多个敏感人口统计学属性的患者进行公平性处理的研究仍较为罕见，而这正是真实临床应用中一个关键且具有挑战性的问题。本文提出了一种面向多敏感属性的新型公平表示学习方法。我们通过在表示空间中实现正交性，追求目标表示与多敏感表示之间的独立性。具体而言，在列空间层面，我们通过在低秩敏感补空间上保留目标信息来强制列空间正交性；在行空间层面，我们鼓励目标表示与敏感表示的特征维度相互正交。通过在CheXpert数据集上进行的大量实验，验证了所提方法的有效性。据我们所知，这是医学影像领域中首个针对多个敏感属性缓解不公平性的研究工作。