Anomaly Detection in Chest X-ray Images with Adversarial Masked Autoencoder

Chest X-ray is the most commonly used detection method for lung diseases, but manual screening often has omissions, so computer-aided diagnosis of chest X-ray abnormalities is necessary. However, since abnormal data relies on expert annotation and is difficult to obtain, unsupervised anomaly detection (UAD) using only normal data has become the focus of attention in the field of medical images. Current UAD methods based on reconstruction often use reconstruction error of original image and reconstructed image as the anomaly score, but the strong reconstruction ability of the autoencoder results in small abnormal image reconstruction error, which is similar to the normal image reconstruction error, making the detection results not satisfactory. Therefore, we proposed CGMAE. In the training stage, masked autoencoder is used as the generator to reconstruct the image, and a discriminator with the reconstructed image and the original image as input is added at the end. Through adversarial learning, the discriminator learns the distribution of normal data. In the testing stage, Gaussian distribution is used to construct the anomaly score, increasing the gap between normal and abnormal data, which is conducive to separating abnormal data from normal data. At the same time, it was found that the current reconstruction models for chest X-ray anomaly detection did not consider the different importance of chest X-ray foreground and background when reconstructing images better. Therefore, we designed a regional weighted loss to enable the generator to reconstruct high-resolution chest X-ray images and enhance the discriminator’s ability to learn data distribution. Experiments on two public datasets, Zhanglab dataset and Chexpert dataset, show that CGMAE exceeds SOTA by 1.41% and 0.98% in AUC metrics respectively.