CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion
CompDiff tackles unfair medical image generation by treating demographic identity as a composition of simple attributes and their interactions. It uses a Hierarchical Conditioner Network to decompose demographics into single, pairwise, and composed representations. This allows the model to generalize effectively to rare or unseen combinations of demographic groups.