Using diffusion models to generate synthetic labeled data for medical image segmentation

Daniel G. Saragih, Atsuhiro Hibi, Pascal N. Tyrrell

Abstract: Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity.