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arXiv 2605.24253cs.CVcs.AIcs.IR

CRISP -- 基于聚类的冗余减少实例采样用于病理病例表示与检索

CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval

Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA（Kimia实验室，人工智能与信息学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；DICE Lab, Department of Electrical and Computer Engineering, University of Illinois Chicago, IL, USA（DICE实验室，电气与计算机工程系，伊利诺伊大学芝加哥分校，伊利诺伊州，美国）；MD Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA（MD Kimia实验室，人工智能与信息学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；PhD Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA（PhD Kimia实验室，人工智能与信息学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；Division of Computational Pathology and Informatics, Mayo Clinic, Rochester, MN, USA（计算病理学与信息学部，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA（实验室医学与病理学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Breast and Melanoma Surgical Oncology, Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA（乳腺和黑色素瘤外科肿瘤学系，综合癌症中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Oncology, Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA（肿瘤学系，综合癌症中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；PhD H.R. Tizhoosh

Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly, Wenchao Han, Saba Yasir, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H. R. Tizhoosh

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AI总结：

提出CRISP无监督框架，通过聚类和冗余减少采样整合病例内多张全切片图像，构建紧凑代表性补丁集用于病例级检索，在乳腺癌数据集上匹配或超越现有标准。

摘要：

数字病理档案中每个病例通常包含多张全切片图像（WSI），这些图像捕获空间上不同的肿瘤区域并反映内在的形态异质性。然而，现有方法大多依赖单一病理学家选择的切片，从而丢弃了分布在其余WSI中的潜在信息性证据。迄今为止，尚无自主框架用于全面的多WSI病例处理。在此，我们提出一个用于病例级分析的无监督框架，该框架整合病例内所有可用切片的信息。所提方法不依赖单一指定切片，而是通过选择性提炼跨WSI的信息性补丁来构建病例级表示。我们引入基于聚类的冗余减少实例采样用于病理学（CRISP），这是一个两阶段框架，首先减少单个WSI内的冗余，随后应用基于聚类的采样为整个病例选择紧凑但具有代表性的补丁集。所得补丁集捕获病例级异质性，同时避免对千兆像素图像的穷举处理，并直接作为检索索引。使用两个梅奥诊所乳腺癌数据集进行诊断和治疗规划，我们证明CRISP在患者/病例搜索和检索中一致匹配或超越当前结合模型和病理学家切片选择的标准实践。通过自动化病例级处理并消除主观WSI选择，CRISP可能能够利用当前被忽视的分布在多个WSI中的临床相关信息。

英文摘要：

Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumor regions and reflecting intrinsic morphological heterogeneity. However, most existing approaches rely on a single pathologist-selected slide, thereby discarding potentially informative evidence distributed across the remaining WSIs. To date, no autonomous framework has been proposed for comprehensive multi-WSI case processing. Here, we present an unsupervised framework for case-level analysis that integrates information from all available slides within a case. Rather than relying on a single designated slide, the proposed approach constructs case-level representations by selectively distilling informative patches across WSIs. We introduce Clustering-Based Redundancy-Reduced Instance Sampling for Pathology (CRISP), a two-stage framework that first reduces redundancy within individual WSIs and subsequently applies clustering-based sampling to select a compact yet representative set of patches for the entire case. The resulting patch set captures case-level heterogeneity while avoiding exhaustive processing of gigapixel images, and directly serves as a retrieval index. Using two Mayo Clinic breast cancer datasets for diagnosis and treatment planning, we demonstrate that CRISP consistently matches or surpasses the current standard practice of combined model and pathologist slide selection for patient/case search and retrieval. By automating case-level processing and eliminating subjective WSI selection, CRISP potentially enables the exploitation of clinically relevant information distributed across multiple WSIs that is currently overlooked.

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。

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