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Dermatological classification algorithms developed without sufficientlydiverse training data may generalize poorly across populations. Whileintentional data collection and annotation offer the best means for improvingrepresentation, new computational approaches for generating training data mayalso aid in mitigating the effects of sampling bias. In this paper, we showthat DALL$\cdot$E 2, a large-scale text-to-image diffusion model, can producephotorealistic images of skin disease across skin types. Using the Fitzpatrick17k dataset as a benchmark, we demonstrate that augmenting training data withDALL$\cdot$E 2-generated synthetic images improves classification of skindisease overall and especially for underrepresented groups.

Improving dermatology classifiers across populations using images generated by large diffusion models