iMTE

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery Abstract1. Models are typically based on large amount of data with annotated examples of known markers aiming at automating detection.2. Unsupervised learning to identify anomalies in imaging data as candidates for markers.3. AnoGan, a deep convolutional generative adversarial network to learn a manifol..