Rethinking fairness in medical imaging: Maximizing group-specific performance with application to skin disease diagnosis.

Abstract

Recent efforts in medical image computing have focused on improving fairness by balancing it with accuracy within a single, unified model. However, this often creates a trade-off: gains for underrepresented groups can come at the expense of reduced accuracy for groups that were previously well-served. In high-stakes clinical contexts, even minor drops in accuracy can lead to serious consequences, making such trade-offs highly contentious. Rather than accepting this compromise, we reframe the fairness objective in this paper as maximizing diagnostic accuracy for each patient group by leveraging additional computational resources to train group-specific models. To achieve this goal, we introduce SPARE, a novel data reweighting algorithm designed to optimize performance for a given group. SPARE evaluates the value of each training sample using two key factors: utility, which reflects the sample's contribution to refining the model's decision boundary, and group similarity, which captures its relevance to the target group. By assigning greater weight to samples that score highly on both metrics, SPARE rebalances the training process-particularly leveraging the value of out-of-group data-to improve group-specific accuracy while avoiding the traditional fairness-accuracy trade-off. Experiments on two skin disease datasets demonstrate that SPARE significantly improves group-specific performance while maintaining comparable fairness metrics, highlighting its promise as a more practical fairness paradigm for improving clinical reliability.