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:在医学图像分析中,减少对机器学习模式的歧视已日益引起注意,然而,稀有的工作侧重于对具有多种敏感人口特征的病人的公平治疗,这是现实世界临床应用中一个至关重要但具有挑战性的问题。我们在本文件中提出了在多敏感属性方面公平代表性学习的新颖方法。我们通过在代表空间实现异位,追求目标与多敏感表达方式的独立性。具体地说,我们通过保持关于低级别敏感空间补充物的目标信息,强制执行柱体空间或孔度。此外,在行空间中,我们鼓励目标与敏感表达方式之间的特征尺寸是垂直的。拟议方法的有效性在CheXpert数据集的广泛实验中得到了证明。据我们所知,这是减少医学成像领域多重敏感属性的不公正性的首项工作。