URCDM: Ultra-Resolution Image Synthesis in Histopathology

TL;DR

URCDMs are a novel generative approach that synthesizes high-resolution histopathology images across multiple resolutions, capturing detailed anatomy and pathology, and outperforming existing models in realism and hierarchical structure.

Contribution

We introduce URCDMs, the first cascaded diffusion models capable of generating entire histopathology images at ultra-high resolutions with multi-resolution consistency.

Findings

Outperforms state-of-the-art multi-resolution models

Expert evaluation shows generated images are indistinguishable from real ones

Effective across brain, breast, and kidney tissue datasets

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.