bibtext: @InProceedings{ Cec_URCDM_MICCAI2024,
                   author = { Cechnicka, Sarah and Ball, James and Baugh, Matthew and Reynaud, Hadrien and Simmonds, Naomi and Smith, Andrew P.T. and Horsfield, Catherine and Roufosse, Candice and Kainz, Bernhard },
                   title = { { URCDM: Ultra-Resolution Image Synthesis in Histopathology } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Diagnosing medical conditions from histopathology data requires a thorough analysis across the various resolutions of Whole Slide Images (WSI). However, existing generative methods fail to consistently represent the hierarchical structure of WSIs due to a focus on high-fidelity patches. To tackle this, we propose Ultra-Resolution Cascaded Diffusion Models (URCDMs) which are capable of synthesising entire histopathology images at high resolutions whilst authentically capturing the details of both the underlying anatomy and pathology at all magnification levels. We evaluate our method on three separate datasets, consisting of brain, breast and kidney tissue, and surpass existing state-of-the-art multi-resolution models. Furthermore, an expert evaluation study was conducted, demonstrating that URCDMs consistently generate outputs across various resolutions that trained evaluators cannot distinguish from real images. All code and additional examples can be found on GitHub.
title: URCDM: Ultra-Resolution Image Synthesis in Histopathology
authors: Cechnicka, Sarah Ball, James Baugh, Matthew Reynaud, Hadrien Simmonds, Naomi Smith, Andrew P.T. Horsfield, Catherine Roufosse, Candice Kainz, Bernhard