  Creating in-silico data with generative AI promises a cost-effective
alternative to staining, imaging, and annotating whole slide images in
computational pathology. Diffusion models are the state-of-the-art solution for
generating in-silico images, offering unparalleled fidelity and realism. Using
appearance transfer diffusion models allows for zero-shot image generation,
facilitating fast application and making model training unnecessary. However
current appearance transfer diffusion models are designed for natural images,
where the main task is to transfer the foreground object from an origin to a
target domain, while the background is of insignificant importance. In
computational pathology, specifically in oncology, it is however not
straightforward to define which objects in an image should be classified as
foreground and background, as all objects in an image may be of critical
importance for the detailed understanding the tumor micro-environment. We
contribute to the applicability of appearance transfer diffusion models to
immunohistochemistry-stained images by modifying the appearance transfer
guidance to alternate between class-specific AdaIN feature statistics matchings
using existing segmentation masks. The performance of the proposed method is
demonstrated on the downstream task of supervised epithelium segmentation,
showing that the number of manual annotations required for model training can
be reduced by 75%, outperforming the baseline approach. Additionally, we
consulted with a certified pathologist to investigate future improvements. We
anticipate this work to inspire the application of zero-shot diffusion models
in computational pathology, providing an efficient method to generate in-silico
images with unmatched fidelity and realism, which prove meaningful for
downstream tasks, such as training existing deep learning models or finetuning
foundation models.
