arXiv 2506.192342025-06-24eess.IVcs.CV

数字病理学中异常检测方法的定量基准测试

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology

Department of Computer Science Vanderbilt University, TN, USA（计算机科学系范德堡大学）；Department of Radiology Weill Cornell Medicine, NY, USA（放射学系韦尔·科恩医学中心）；Department of Medicine Vanderbilt University Medical Center, TN, USA Veterans Affairs Tennessee Valley Healthcare System TN, USA（医学系范德堡大学医学中心退伍军人事务田纳西山谷医疗系统）；Department of Medicine Vanderbilt University Medical Center Veterans Affairs Tennessee Valley Healthcare System TN, USA（医学系范德堡大学医学中心退伍军人事务田纳西山谷医疗系统）；Department of Anatomical Pathology UT MD Anderson Cancer Center,TX, USA（解剖病理学系UT MD安德森癌症中心）

Can Cui, Xindong Zheng, Ruining Deng, Quan Liu, Tianyuan Yao, Keith T Wilson, Lori A Coburn, Bennett A Landman, Haichun Yang, Yaohong Wang, Yuankai Huo

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AI总结：

该研究针对数字病理学图像的独特挑战，对20多种经典异常检测方法在五个真实和合成数据集上进行了定量基准测试，评估了图像尺度、异常类型和训练策略对性能的影响，为未来研究提供了综合基准。

摘要：

异常检测在工业缺陷检测背景下已被广泛研究，已开发出众多方法来应对各种挑战。在数字病理学中，异常检测在罕见病识别、伪影检测和生物标志物发现等应用中具有巨大潜力。然而，病理图像的独特特征，如其大尺寸、多尺度结构、染色变异性和重复模式，引入了当前异常检测算法难以解决的新挑战。在这项定量研究中，我们通过大量实验对20多种经典和流行的异常检测方法进行了基准测试。我们整理了五个真实和合成的数字病理学数据集，以系统地评估这些方法。我们的实验研究了图像尺度、异常模式类型和训练轮次选择策略对检测性能的影响。结果详细比较了每种方法的优缺点，建立了一个综合基准，以指导未来数字病理学图像异常检测的研究。

英文摘要：

Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges. In digital pathology, anomaly detection holds significant potential for applications such as rare disease identification, artifact detection, and biomarker discovery. However, the unique characteristics of pathology images, such as their large size, multi-scale structures, stain variability, and repetitive patterns, introduce new challenges that current anomaly detection algorithms struggle to address. In this quantitative study, we benchmark over 20 classical and prevalent anomaly detection methods through extensive experiments. We curated five digital pathology datasets, both real and synthetic, to systematically evaluate these approaches. Our experiments investigate the influence of image scale, anomaly pattern types, and training epoch selection strategies on detection performance. The results provide a detailed comparison of each method's strengths and limitations, establishing a comprehensive benchmark to guide future research in anomaly detection for digital pathology images.

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