  Tumor segmentation in histopathology images is often complicated by its
composition of different histological subtypes and class imbalance.
Oversampling subtypes with low prevalence features is not a satisfactory
solution since it eventually leads to overfitting. We propose to create
synthetic images with semantically-conditioned deep generative networks and to
combine subtype-balanced synthetic images with the original dataset to achieve
better segmentation performance. We show the suitability of Generative
Adversarial Networks (GANs) and especially diffusion models to create realistic
images based on subtype-conditioning for the use case of HER2-stained
histopathology. Additionally, we show the capability of diffusion models to
conditionally inpaint HER2 tumor areas with modified subtypes. Combining the
original dataset with the same amount of diffusion-generated images increased
the tumor Dice score from 0.833 to 0.854 and almost halved the variance between
the HER2 subtype recalls. These results create the basis for more reliable
automatic HER2 analysis with lower performance variance between individual HER2
subtypes.
