  Poor performance of quantitative analysis in histopathological Whole Slide
Images (WSI) has been a significant obstacle in clinical practice. Annotating
large-scale WSIs manually is a demanding and time-consuming task, unlikely to
yield the expected results when used for fully supervised learning systems.
Rarely observed disease patterns and large differences in object scales are
difficult to model through conventional patient intake. Prior methods either
fall back to direct disease classification, which only requires learning a few
factors per image, or report on average image segmentation performance, which
is highly biased towards majority observations. Geometric image augmentation is
commonly used to improve robustness for average case predictions and to enrich
limited datasets. So far no method provided sampling of a realistic posterior
distribution to improve stability, e.g. for the segmentation of imbalanced
objects within images. Therefore, we propose a new approach, based on diffusion
models, which can enrich an imbalanced dataset with plausible examples from
underrepresented groups by conditioning on segmentation maps. Our method can
simply expand limited clinical datasets making them suitable to train machine
learning pipelines, and provides an interpretable and human-controllable way of
generating histopathology images that are indistinguishable from real ones to
human experts. We validate our findings on two datasets, one from the public
domain and one from a Kidney Transplant study.
