In medical image diagnosis, fairness has become increasingly crucial. Without
bias mitigation, deploying unfair AI would harm the interests of the
underprivileged population and potentially tear society apart. Recent research
addresses prediction biases in deep learning models concerning demographic
groups (e.g., gender, age, and race) by utilizing demographic (sensitive
attribute) information during training. However, many sensitive attributes
naturally exist in dermatological disease images. If the trained model only
targets fairness for a specific attribute, it remains unfair for other
attributes. Moreover, training a model that can accommodate multiple sensitive
attributes is impractical due to privacy concerns. To overcome this, we propose
a method enabling fair predictions for sensitive attributes during the testing
phase without using such information during training. Inspired by prior work
highlighting the impact of feature entanglement on fairness, we enhance the
model features by capturing the features related to the sensitive and target
attributes and regularizing the feature entanglement between corresponding
classes. This ensures that the model can only classify based on the features
related to the target attribute without relying on features associated with
sensitive attributes, thereby improving fairness and accuracy. Additionally, we
use disease masks from the Segment Anything Model (SAM) to enhance the quality
of the learned feature. Experimental results demonstrate that the proposed
method can improve fairness in classification compared to state-of-the-art
methods in two dermatological disease datasets.

在医学图像诊断中，公平性变得越来越重要。本文提出了一种方法，在测试阶段实现对敏感属性的公平预测，而无需在训练过程中使用此类信息，并通过增强模型特征和规范特征的纠缠关系来提高公平性和准确性。实验结果表明，在两个皮肤病数据集中，该方法能够提高分类的公平性。