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.
@article{arxiv.2312.01152,
title = {Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology},
author = {Sarah Cechnicka and Hadrien Reynaud and James Ball and Naomi Simmonds and Catherine Horsfield and Andrew Smith and Candice Roufosse and Bernhard Kainz},
journal= {arXiv preprint arXiv:2312.01152},
year = {2023}
}