Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. However, with the increasing prevalence of foundation models in medical image analysis, it is hard to train fair models from scratch while preserving utility. In this paper, we propose a novel method, Adversarial Privacy-aware Perturbations on Latent Embedding (APPLE), that can improve the fairness of deployed segmentors by introducing a small latent feature perturber without updating the weights of the original model. By adding perturbation to the latent vector, APPLE decorates the latent vector of segmentors such that no fairness-related features can be passed to the decoder of the segmentors while preserving the architecture and parameters of the segmentor. Experiments on two segmentation datasets and five segmentors (three U-Net-like and two SAM-like) illustrate the effectiveness of our proposed method compared to several unfairness mitigation methods.

通过在潜在嵌入上引入微小的扰动，而不更新原始模型的权重，我们提出了一种新的方法APPLE（Adversarial Privacy-aware Perturbations on Latent Embedding），以提高部署分割器的公平性，从而在保留分割器的架构和参数的同时，对分割器的潜在矢量进行装饰，以便不能传递与公平性相关的特征给分割器的解码器。与几种不公平性缓解方法相比，通过在两个分割数据集和五个分割器上进行的实验证明了我们提出的方法的有效性。

APPLE：隐嵌入的对抗性隐私感知扰动用于公平性缓解