Fairness has become increasingly pivotal in medical image recognition. However, without mitigating bias, deploying unfair medical AI systems could harm the interests of underprivileged populations. In this paper, we observe that while features extracted from the deeper layers of neural networks generally offer higher accuracy, fairness conditions deteriorate as we extract features from deeper layers. This phenomenon motivates us to extend the concept of multi-exit frameworks. Unlike existing works mainly focusing on accuracy, our multi-exit framework is fairness-oriented; the internal classifiers are trained to be more accurate and fairer, with high extensibility to apply to most existing fairness-aware frameworks. During inference, any instance with high confidence from an internal classifier is allowed to exit early. Experimental results show that the proposed framework can improve the fairness condition over the state-of-the-art in two dermatological disease datasets.

翻译：公平性在医学图像识别中日益重要。然而，若未缓解偏差，部署不公平的医学人工智能系统可能损害弱势群体的利益。本文观察到，尽管从神经网络深层提取的特征通常具有更高准确性，但随着特征提取层数的加深，公平性条件反而恶化。这一现象促使我们扩展多出口框架的概念。与现有主要关注准确性的工作不同，我们的多出口框架以公平性为导向：内部分类器经过训练后兼具更高准确性与公平性，且具备高度可扩展性，可适配大多数现有公平感知框架。在推理阶段，任何内部分类器输出高置信度的实例均可提前退出。实验结果表明，所提框架在两个皮肤病数据集上能够改善公平性条件，超越当前最优水平。