AI-based diagnoses have demonstrated dermatologist-level performance in
classifying skin cancer. However, such systems are prone to under-performing
when tested on data from minority groups that lack sufficient representation in
the training sets. Although data collection and annotation offer the best means
for promoting minority groups, these processes are costly and time-consuming.
Prior works have suggested that data from majority groups may serve as a
valuable information source to supplement the training of diagnosis tools for
minority groups. In this work, we propose an effective diffusion-based
augmentation framework that maximizes the use of rich information from majority
groups to benefit minority groups. Using groups with different skin types as a
case study, our results show that the proposed framework can generate synthetic
images that improve diagnostic results for the minority groups, even when there
is little or no reference data from these target groups. The practical value of
our work is evident in medical imaging analysis, where under-diagnosis persists
as a problem for certain groups due to insufficient representation.

通过有效的扩散增强框架，利用多数群体的丰富信息来改善少数群体的诊断结果，对于皮肤癌等疾病的医学成像分析具有实际价值。