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.

翻译：在医学图像诊断中，公平性已变得日益重要。若不采取偏差缓解措施，部署不公平的AI将损害弱势群体的利益，甚至可能造成社会撕裂。现有研究通过利用训练过程中的人口统计信息（如性别、年龄和种族等敏感属性），解决深度学习模型在人口统计群体间的预测偏差问题。然而，皮肤病图像中自然存在多种敏感属性。若训练模型仅针对单一属性实现公平，则对其他属性仍存在不公平性。此外，出于隐私保护考量，训练能兼容多种敏感属性的模型并不现实。为解决这一问题，我们提出了一种方法：无需在训练阶段使用敏感属性信息，即可在测试阶段实现针对这些属性的公平预测。受先前研究揭示特征纠缠对公平性影响的启发，我们通过捕获与敏感属性和目标属性相关的特征，并规范化对应类别间的特征纠缠来增强模型特征。这确保模型能仅基于目标属性相关特征进行分类，而不依赖敏感属性相关特征，从而提升公平性与准确性。此外，我们利用分割一切模型（SAM）生成的疾病掩膜增强学习特征的质量。实验结果表明，在两个皮肤病数据集上，所提方法相比现有最优方法能进一步改善分类公平性。