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We present a novel diffusion-based approach to generate synthetichistopathological Whole Slide Images (WSIs) at an unprecedented gigapixelscale. Synthetic WSIs have many potential applications: They can augmenttraining datasets to enhance the performance of many computational pathologyapplications. They allow the creation of synthesized copies of datasets thatcan be shared without violating privacy regulations. Or they can facilitatelearning representations of WSIs without requiring data annotations. Despitethis variety of applications, no existing deep-learning-based method generatesWSIs at their typically high resolutions. Mainly due to the high computationalcomplexity. Therefore, we propose a novel coarse-to-fine sampling scheme totackle image generation of high-resolution WSIs. In this scheme, we increasethe resolution of an initial low-resolution image to a high-resolution WSI.Particularly, a diffusion model sequentially adds fine details to images andincreases their resolution. In our experiments, we train our method with WSIsfrom the TCGA-BRCA dataset. Additionally to quantitative evaluations, we alsoperformed a user study with pathologists. The study results suggest that ourgenerated WSIs resemble the structure of real WSIs.

Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale