URCDM: Ultra-Resolution Image Synthesis in Histopathology
<|reference_start|>URCDM: Ultra-Resolution Image Synthesis in Histopathology: 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.<|reference_end|>
@article{cechnicka2024urcdm:,
    title={URCDM: Ultra-Resolution Image Synthesis in Histopathology},
    author={Sarah Cechnicka, James Ball, Matthew Baugh, Hadrien Reynaud, Naomi
  Simmonds, Andrew P.T. Smith, Catherine Horsfield, Candice Roufosse, and
  Bernhard Kainz},
    journal={arXiv preprint arXiv:2407.13277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13277},
    primaryClass={eess.IV cs.CV}
}