Abstract
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 diagnostic 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. Our implementation detail is available at https://github.com/janet-sw/skin-diff.
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Acknowledgments
This work was partly supported by the NSF EPSCoR-Louisiana Materials Design Alliance (LAMDA) program #OIA-1946231 and partly by the Harold L. and Heather E. Jurist Center of Excellence for Artificial Intelligence at Tulane University.
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Wang, J., Chung, Y., Ding, Z., Hamm, J. (2025). From Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis. In: Celebi, M.E., Reyes, M., Chen, Z., Li, X. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops. MICCAI 2024. Lecture Notes in Computer Science, vol 15274. Springer, Cham. https://doi.org/10.1007/978-3-031-77610-6_2
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