Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder

This paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial AUTO, and yields superior performance to state-of-the-art methods.