arXiv 2508.152362025-08-21eess.IVcs.CV

病理信息引导的潜在扩散模型用于淋巴结转移中的异常检测

Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis

School of Electrical Engineering, Korea University, Seoul 02841, Korea（韩国大学电子工程学院）；Department of Pathology, Korea University Anam Hospital and Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02841, Korea（韩国大学医学院病理学系）；Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea（韩国天主大学医学院圣玛丽医院医院病理学系）

Jiamu Wang, Keunho Byeon, Jinsol Song, Anh Nguyen, Sangjeong Ahn, Sung Hak Lee, Jin Tae Kwak

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AI总结：

本文提出一种结合视觉-语言模型与扩散模型的无监督异常检测方法，利用病理学关键词引导重建以区分正常与异常淋巴结组织，并在胃和乳腺淋巴结数据集上验证了其有效性和跨器官泛化能力。

摘要：

异常检测是数字病理学中一种新兴的方法，因其能够高效且有效地利用数据进行疾病诊断。虽然监督学习方法能够提供高精度，但它们依赖于大量标注的数据集，在数字病理学中面临数据稀缺的问题。然而，无监督异常检测通过识别与正常组织分布的偏差，提供了一种可行的替代方案，而无需进行详尽的标注。近年来，去噪扩散概率模型在无监督异常检测中越来越受欢迎，在自然图像和医学影像数据集上都取得了令人鼓舞的性能。在此基础上，我们将视觉-语言模型与扩散模型相结合，用于数字病理学中的无监督异常检测，在重建过程中利用组织病理学提示。我们的方法采用一组与正常组织相关的病理学关键词来引导重建过程，从而促进正常组织与异常组织之间的区分。为了评估所提出方法的有效性，我们在来自本地医院的胃淋巴结数据集上进行了实验，并使用公开的乳腺淋巴结数据集评估了其在领域偏移下的泛化能力。实验结果凸显了所提出方法在数字病理学中跨多种器官进行无监督异常检测的潜力。代码：https://github.com/QuIIL/AnoPILaD。

英文摘要：

Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approaches deliver high accuracy, they rely on extensively annotated datasets, suffering from data scarcity in digital pathology. Unsupervised anomaly detection, however, offers a viable alternative by identifying deviations from normal tissue distributions without requiring exhaustive annotations. Recently, denoising diffusion probabilistic models have gained popularity in unsupervised anomaly detection, achieving promising performance in both natural and medical imaging datasets. Building on this, we incorporate a vision-language model with a diffusion model for unsupervised anomaly detection in digital pathology, utilizing histopathology prompts during reconstruction. Our approach employs a set of pathology-related keywords associated with normal tissues to guide the reconstruction process, facilitating the differentiation between normal and abnormal tissues. To evaluate the effectiveness of the proposed method, we conduct experiments on a gastric lymph node dataset from a local hospital and assess its generalization ability under domain shift using a public breast lymph node dataset. The experimental results highlight the potential of the proposed method for unsupervised anomaly detection across various organs in digital pathology. Code: https://github.com/QuIIL/AnoPILaD.

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