High-quality semi-supervised anomaly detection with generative adversarial networks.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
Nov 9, 2023
Abstract
PURPOSE: The visualization of an anomaly area is easier in anomaly detection methods that use generative models rather than classification models. However, achieving both anomaly detection accuracy and a clear visualization of anomalous areas is challenging. This study aimed to establish a method that combines both detection accuracy and clear visualization of anomalous areas using a generative adversarial network (GAN).