Self-supervised anomaly detection of medical images based on dual-module discrepancy

Medical images anomaly detection plays a very important role in modern health care, which helps to improve the quality and efficiency of medical services and promote the development of human health. Due to the high cost of annotation in anomaly images and the fact that most existing methods do not fully utilize information from unlabeled images. Therefore, we propose a new reconstruction network and loss function that can better utilize unlabelled and normal images for anomaly identification. The framework used in this paper consists of two modules, each consisting of three reconstruction networks with the same architecture but different inputs. One module is trained only on normal images and is called the normal module (NM). The other module is trained on both normal images and unlabeled images, and is called the unknown module (UM). Furthermore, the internal differences of the normal module and the differences between the two modules will be used as two powerful anomaly scores, and these two anomaly scores will be refined to indicate anomalies. Experiments on four medical datasets show the state-of-the-art performance by the proposed approach.