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Image synthesis approaches, e.g., generative adversarial networks, have beenpopular as a form of data augmentation in medical image analysis tasks. It isprimarily beneficial to overcome the shortage of publicly accessible data andassociated quality annotations. However, the current techniques often lackcontrol over the detailed contents in generated images, e.g., the type ofdisease patterns, the location of lesions, and attributes of the diagnosis. Inthis work, we adapt the latest advance in the generative model, i.e., thediffusion model, with the added control flow using lesion-specific visual andtextual prompts for generating dermatoscopic images. We further demonstrate theadvantage of our diffusion model-based framework over the classical generationmodels in both the image quality and boosting the segmentation performance onskin lesions. It can achieve a 9% increase in the SSIM image quality measureand an over 5% increase in Dice coefficients over the prior arts.

Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts