Diffusion-based generation of Histopathological Whole Slide Images at a
  Gigapixel scale
Robert Harb Thomas Pock Heimo Müller
We present a novel diffusion-based approach to generate synthetic
histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel
scale. Synthetic WSIs have many potential applications: They can augment
training datasets to enhance the performance of many computational pathology
applications. They allow the creation of synthesized copies of datasets that
can be shared without violating privacy regulations. Or they can facilitate
learning representations of WSIs without requiring data annotations. Despite
this variety of applications, no existing deep-learning-based method generates
WSIs at their typically high resolutions. Mainly due to the high computational
complexity. Therefore, we propose a novel coarse-to-fine sampling scheme to
tackle image generation of high-resolution WSIs. In this scheme, we increase
the resolution of an initial low-resolution image to a high-resolution WSI.
Particularly, a diffusion model sequentially adds fine details to images and
increases their resolution. In our experiments, we train our method with WSIs
from the TCGA-BRCA dataset. Additionally to quantitative evaluations, we also
performed a user study with pathologists. The study results suggest that our
generated WSIs resemble the structure of real WSIs.