中文标题#
病理學引導的潛在擴散模型用於淋巴結轉移中的異常檢測
英文标题#
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
中文摘要#
異常檢測是一種在數字病理學中新興的方法，因其能夠高效且有效地利用數據進行疾病診斷。 雖然監督學習方法可以實現高準確性，但它們依賴於廣泛標註的數據集，在數字病理學中存在數據稀缺的問題。 然而，無監督異常檢測提供了一種可行的替代方法，通過識別正常組織分佈的偏差來實現，而無需進行全面的標註。 最近，去噪擴散概率模型在無監督異常檢測中獲得了流行，並在自然和醫學影像數據集中實現了有前景的性能。 在此基礎上，我們將視覺 - 語言模型與擴散模型結合，用於數字病理學中的無監督異常檢測，在重建過程中利用組織病理學提示。 我們的方法使用一組與正常組織相關的病理學術語來指導重建過程，有助於區分正常和異常組織。 為了評估所提出方法的有效性，我們在本地醫院的胃淋巴結數據集上進行了實驗，並使用公開的乳腺淋巴結數據集評估其在領域偏移下的泛化能力。 實驗結果突顯了所提出方法在數字病理學中各種器官無監督異常檢測的潛力。 代碼：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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