arXiv 2407.132772024-07-18eess.IVcs.CV

URCDM：组织病理学中的超分辨率图像合成

URCDM: Ultra-Resolution Image Synthesis in Histopathology

Imperial College London（伦敦帝国学院）；NHS Trust（英国国家医疗服务体系信托机构）；Friedrich–Alexander University Erlangen–Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）

Sarah Cechnicka, James Ball, Matthew Baugh, Hadrien Reynaud, Naomi Simmonds, Andrew P. T. Smith, Catherine Horsfield, Candice Roufosse, Bernhard Kainz

备注：: arXiv admin note: text overlap with arXiv:2312.01152

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AI总结：

针对现有生成方法无法一致表示全切片图像层次结构的问题，提出超分辨率级联扩散模型（URCDM），能高分辨率合成完整组织病理学图像，在三个数据集上超越现有最先进模型，且专家无法区分生成图像与真实图像。

摘要：

从组织病理学数据诊断医学状况需要对全切片图像（WSI）的各种分辨率进行全面分析。然而，现有的生成方法由于专注于高保真补丁，无法一致地表示WSI的层次结构。为了解决这一问题，我们提出了超分辨率级联扩散模型（URCDMs），该模型能够以高分辨率合成整个组织病理学图像，同时真实地捕捉所有放大倍数下底层解剖结构和病理学的细节。我们在三个独立数据集上评估了我们的方法，这些数据集包含脑、乳腺和肾组织，并超越了现有的最先进的多分辨率模型。此外，我们还进行了一项专家评估研究，表明URCDMs在各种分辨率下一致地生成输出，训练有素的评估者无法将其与真实图像区分开来。所有代码和额外示例均可在GitHub上找到。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。