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Poor performance of quantitative analysis in histopathological Whole SlideImages (WSI) has been a significant obstacle in clinical practice. Annotatinglarge-scale WSIs manually is a demanding and time-consuming task, unlikely toyield the expected results when used for fully supervised learning systems.Rarely observed disease patterns and large differences in object scales aredifficult to model through conventional patient intake. Prior methods eitherfall back to direct disease classification, which only requires learning a fewfactors per image, or report on average image segmentation performance, whichis highly biased towards majority observations. Geometric image augmentation iscommonly used to improve robustness for average case predictions and to enrichlimited datasets. So far no method provided sampling of a realistic posteriordistribution to improve stability, e.g. for the segmentation of imbalancedobjects within images. Therefore, we propose a new approach, based on diffusionmodels, which can enrich an imbalanced dataset with plausible examples fromunderrepresented groups by conditioning on segmentation maps. Our method cansimply expand limited clinical datasets making them suitable to train machinelearning pipelines, and provides an interpretable and human-controllable way ofgenerating histopathology images that are indistinguishable from real ones tohuman experts. We validate our findings on two datasets, one from the publicdomain and one from a Kidney Transplant study.

Realistic Data Enrichment for Robust Image Segmentation in Histopathology