URCDM: Ultra-Resolution Image Synthesis in Histopathology
Cechnicka S, Ball J, Baugh M, Reynaud H, Simmonds N, Smith AP, Horsfield C, Roufosse C, Kainz B (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15004 LNCS
Pages Range: 535-545
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Marrakesh, MAR
ISBN: 9783031720826
DOI: 10.1007/978-3-031-72083-3_50
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.
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United Kingdom (GB)How to cite
APA:
Cechnicka, S., Ball, J., Baugh, M., Reynaud, H., Simmonds, N., Smith, A.P.,... Kainz, B. (2024). URCDM: Ultra-Resolution Image Synthesis in Histopathology. In Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 535-545). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
MLA:
Cechnicka, Sarah, et al. "URCDM: Ultra-Resolution Image Synthesis in Histopathology." Proceedings of the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, Marrakesh, MAR Ed. Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel, Springer Science and Business Media Deutschland GmbH, 2024. 535-545.
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