用增量学习提升皮肤病诊断公平性，兼顾准确率与公正性
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
- 基于领域增量学习动态适应数据分布变化，平衡不同群体学习
- 在两个皮肤数据集上同时提升公平性与模型性能
- 适合关注AI医疗公平性的研究者和临床应用开发者
随着深度学习技术的快速发展，人工智能在皮肤病诊断的研究与应用中日益普及。然而，这种数据驱动的方法常面临决策偏差问题。现有公平性增强技术往往以牺牲准确性为代价。本研究旨在实现皮肤病诊断模型在准确率与公平性之间的更好权衡。为此，提出一种名为FairDD的新颖公平皮肤病诊断网络，利用领域增量学习敏感地应对数据分布变化，均衡不同群体的学习。此外，结合mixup数据增强与监督对比学习，提升网络鲁棒性与泛化能力。在两个皮肤病数据集上的实验验证表明，所提方法在公平性指标及公平性与性能的权衡上均表现优异。
原文摘要 · Abstract (English)
With the rapid advancement of deep learning technologies, artificial intelligence has become increasingly prevalent in the research and application of dermatological disease diagnosis. However, this data-driven approach often faces issues related to decision bias. Existing fairness enhancement techniques typically come at a substantial cost to accuracy. This study aims to achieve a better trade-off between accuracy and fairness in dermatological diagnostic models. To this end, we propose a novel fair dermatological diagnosis network, named FairDD, which leverages domain incremental learning to balance the learning of different groups by being sensitive to changes in data distribution. Additionally, we incorporate the mixup data augmentation technique and supervised contrastive learning to enhance the network's robustness and generalization. Experimental validation on two dermatological datasets demonstrates that our proposed method excels in both fairness criteria and the trade-off between fairness and performance.
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