Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis
  in Histopathology
Sarah Cechnicka Hadrien Reynaud James Ball Naomi Simmonds Catherine Horsfield Andrew Smith Candice Roufosse Bernhard Kainz
Diagnoses from histopathology images rely on information from both high and
low resolutions of Whole Slide Images. Ultra-Resolution Cascaded Diffusion
Models (URCDMs) allow for the synthesis of high-resolution images that are
realistic at all magnification levels, focusing not only on fidelity but also
on long-distance spatial coherency. Our model beats existing methods, improving
the pFID-50k [2] score by 110.63 to 39.52 pFID-50k. Additionally, a human
expert evaluation study was performed, reaching a weighted Mean Absolute Error
(MAE) of 0.11 for the Lower Resolution Diffusion Models and a weighted MAE of
0.22 for the URCDM.