計算組織病理學的自動分析技術可幫助病理學家有效進行判讀。 然而，由於腫瘤標記樣本的稀缺性和未知疾病的存在，開發一個針對組織病理影像的全監督學習的穩健模型具有挑戰性。無監督異常檢測（Unsupervised anomaly detection, UAD） 方法主要用於工業檢驗，因此被提出來以促進高效分析。UAD只需要正常樣本進行訓練，大大減輕標註的負擔。在本文中，我們介紹一種基於重建模型的UAD方法，稱為ALSA-UAD，該方法通過對抗學習（adversarial learning）和模擬異常（simulated anomaly）來改進特徵學習。一方面，我們混合從正常圖像中提取的特徵，以構建更平滑的特徵分佈，並使用對抗學習來增強自動編碼器（autoencoder）的圖像重建能力。另一方面，我們通過圖像變形來模擬異常圖像，並引導自動編碼器更好地捕捉圖像的全局特徵。我們同時在病理學和電腦斷層掃描異常檢測基準上展示了其有效性，並顯示出最先進的性能。

Automated analytics in computational histopathology have shown significant progress in aiding pathologists through digital image analysis. However, developing a robust model based on supervised learning for histopathology images is challenging because of the scarcity of tumor-marked samples and unknown diseases. Unsupervised anomaly detection (UAD) methods that were mostly used in industrial inspection are thus proposed to facilitate efficient analytics. UAD only requires normal samples for training and largely reduces the burden of labeling. In this paper, we introduce a reconstruction-based UAD approach called ALSA-UAD to improve representation learning based on adversarial learning and simulated anomalies. On the one hand, we mix up features extracted from normal images to build a smoother feature distribution and employ adversarial learning to enhance an autoencoder for image reconstruction. On the other hand, we simulate anomalous images by image deformation, and guide the autoencoder to catch global characteristics of images well. We demonstrate its effectiveness on histopathology and computed tomography anomaly detection benchmarks and show state-of-the-art performance.

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