FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis

TL;DR

FairDD introduces a domain-incremental learning approach combined with data augmentation and contrastive learning to improve fairness without significantly sacrificing accuracy in dermatological disease diagnosis.

Contribution

The paper presents a novel fairness-aware dermatological diagnosis network, FairDD, that balances accuracy and fairness using domain-incremental learning and advanced augmentation techniques.

Findings

Outperforms existing methods in fairness metrics.

Maintains high diagnostic accuracy.

Demonstrates robustness and generalization on dermatological datasets.

Abstract

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.