The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transformation has given rise to concerns about the fairness of AI, which disproportionately affects certain groups, particularly patients from underprivileged populations. Recently, a number of methods and large-scale datasets have been proposed to address group performance disparities. Although these methods have shown effectiveness in disease classification tasks, they may fall short in ensuring fair prediction of disease progression, mainly because of limited longitudinal data with diverse demographics available for training a robust and equitable prediction model. In this paper, we introduce TransFair to enhance demographic fairness in progression prediction for ocular diseases. TransFair aims to transfer a fairness-enhanced disease classification model to the task of progression prediction with fairness preserved. Specifically, we train a fair EfficientNet, termed FairEN, equipped with a fairness-aware attention mechanism using extensive data for ocular disease classification. Subsequently, this fair classification model is adapted to a fair progression prediction model through knowledge distillation, which aims to minimize the latent feature distances between the classification and progression prediction models. We evaluate FairEN and TransFair for fairness-enhanced ocular disease classification and progression prediction using both two-dimensional (2D) and 3D retinal images. Extensive experiments and comparisons with models with and without considering fairness learning show that TransFair effectively enhances demographic equity in predicting ocular disease progression.

翻译：人工智能在自动化疾病分类中的应用显著降低了医疗成本并提升了服务的可及性。然而，这一转变引发了人们对人工智能公平性的担忧，其不成比例地影响了特定群体，尤其是弱势群体患者。近期，已有多种方法和大规模数据集被提出以应对群体性能差异。尽管这些方法在疾病分类任务中显示出有效性，但在确保疾病进展的公平预测方面可能仍显不足，这主要归因于可用于训练稳健且公平预测模型的、具有多样化人口统计学特征的纵向数据有限。本文提出TransFair，旨在提升眼病进展预测中的人口统计学公平性。TransFair致力于将经过公平性增强的疾病分类模型迁移至进展预测任务，同时保持公平性。具体而言，我们利用大规模眼病分类数据训练了一个配备公平感知注意力机制的公平EfficientNet模型，称为FairEN。随后，通过知识蒸馏将该公平分类模型适配为公平进展预测模型，其目标是最小化分类模型与进展预测模型之间的潜在特征距离。我们使用二维和三维视网膜图像，对FairEN和TransFair在公平性增强的眼病分类及进展预测任务上进行了评估。大量实验及与考虑/未考虑公平性学习模型的对比表明，TransFair能有效提升眼病进展预测中的人口统计学公平性。