# On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations Source: [On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:04:50.062Z Authors: Wenlong Deng, Yuan Zhong, Qi Dou, Xiaoxiao Li Published: 2023-01-04T08:11:11.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()