中文标题#
病理学引导的潜在扩散模型用于淋巴结转移中的异常检测
英文标题#
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
中文摘要#
异常检测是一种在数字病理学中新兴的方法，因其能够高效且有效地利用数据进行疾病诊断。 虽然监督学习方法可以实现高准确性，但它们依赖于广泛标注的数据集，在数字病理学中存在数据稀缺的问题。 然而，无监督异常检测提供了一种可行的替代方法，通过识别正常组织分布的偏差来实现，而无需进行全面的标注。 最近，去噪扩散概率模型在无监督异常检测中获得了流行，并在自然和医学影像数据集中实现了有前景的性能。 在此基础上，我们将视觉 - 语言模型与扩散模型结合，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。 我们的方法使用一组与正常组织相关的病理学术语来指导重建过程，有助于区分正常和异常组织。 为了评估所提出方法的有效性，我们在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其在领域偏移下的泛化能力。 实验结果突显了所提出方法在数字病理学中各种器官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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