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.

使用超高分辨率级联扩散模型（Ultra-Resolution Cascaded Diffusion Models，URCDMs）从组织病理学数据中诊断医学状况，能够在高分辨率下合成整个组织病理学图像，同时真实地捕捉到各个放大倍率水平下底层解剖和病理的细节。

URCDM：组织病理学中的超高分辨率图像合成