Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert annotation of anomaly data. However, nearly all existing methods are formulated as a one-class classification trained only on representations from the normal class and discard a potentially significant portion of the unlabeled data. This paper focuses on a more practical setting, dual distribution anomaly detection for chest X-rays, using the entire training data, including both normal and unlabeled images. Inspired by a modern self-supervised vision transformer model trained using partial image inputs to reconstruct missing image regions -- we propose AMAE, a two-stage algorithm for adaptation of the pre-trained masked autoencoder (MAE). Starting from MAE initialization, AMAE first creates synthetic anomalies from only normal training images and trains a lightweight classifier on frozen transformer features. Subsequently, we propose an adaptation strategy to leverage unlabeled images containing anomalies. The adaptation scheme is accomplished by assigning pseudo-labels to unlabeled images and using two separate MAE based modules to model the normative and anomalous distributions of pseudo-labeled images. The effectiveness of the proposed adaptation strategy is evaluated with different anomaly ratios in an unlabeled training set. AMAE leads to consistent performance gains over competing self-supervised and dual distribution anomaly detection methods, setting the new state-of-the-art on three public chest X-ray benchmarks: RSNA, NIH-CXR, and VinDr-CXR.

翻译：摘要：在胸部X光片等医学图像中，无监督异常检测正逐步成为研究焦点，因其能减少对耗时且昂贵的异常数据专家标注的依赖。然而，现有方法几乎均被设定为单类分类任务——仅基于正常类别表征进行训练，因而丢弃了未标注数据中可能具有重要价值的部分。本文聚焦于更实用的场景——胸部X光双分布异常检测，利用包含正常与未标注图像在内的全部训练数据。受现代自监督视觉Transformer模型通过部分图像输入重建缺失区域的启发，我们提出AMAE，一种用于适配预训练掩码自编码器（MAE）的两阶段算法。以MAE初始化为起点，AMAE首先仅从正常训练图像中生成合成异常样本，并在冻结的Transformer特征上训练轻量级分类器。随后，我们提出一种利用含异常未标注图像的适配策略：通过为未标注图像分配伪标签，并采用两个独立基于MAE的模块分别建模伪标签图像中的规范分布与异常分布。在不同异常比例的未标注训练集上，该适配策略的有效性得到验证。AMAE在性能上持续优于竞争性自监督与双分布异常检测方法，并在三个公开胸部X光基准数据集（RSNA、NIH-CXR、VinDr-CXR）上达到当前最优水平。