FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
This addresses fairness issues in AI for dermatological diagnosis, offering a method to reduce bias without significantly compromising accuracy, though it appears incremental in approach.
The study tackled decision bias in dermatological disease diagnosis models by proposing FairDD, a network using domain incremental learning, mixup augmentation, and supervised contrastive learning, achieving improved fairness and performance trade-offs on two datasets.
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.