An Ordinal Diffusion Model for Generating Medical Images with Different
  Severity Levels
Shumpei Takezaki Seiichi Uchida
Diffusion models have recently been used for medical image generation because
of their high image quality. In this study, we focus on generating medical
images with ordinal classes, which have ordinal relationships, such as severity
levels. We propose an Ordinal Diffusion Model (ODM) that controls the ordinal
relationships of the estimated noise images among the classes. Our model was
evaluated experimentally by generating retinal and endoscopic images of
multiple severity classes. ODM achieved higher performance than conventional
generative models by generating realistic images, especially in high-severity
classes with fewer training samples.