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：减轻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，针对具有多重敏感人口属性患者的公平处理研究鲜有涉足，这成为真实临床应用中的关键性难题。本文提出一种针对多敏感属性的公平表示学习新方法，通过实现表示空间的正交性来追求目标表示与多敏感表示之间的独立性。具体而言，我们在列空间约束低秩敏感补空间内的目标信息保持正交性；同时在行空间促使目标表示与敏感表示的特征维度相互正交。通过在CheXpert数据集上的广泛实验验证了该方法有效性。据我们所知，这是医学成像领域中首项针对多敏感属性缓解不公平性的研究工作。