Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Obtaining models that capture imaging markers relevant for<lb>disease progression and treatment monitoring is challenging. Models are<lb>typically based on large amounts of data with annotated examples of<lb>known markers aiming at automating detection. High annotation ef-<lb>fort and the limitation to a vocabulary of known markers limit the<lb>power of such approaches. Here, we perform unsupervised learning to<lb>identify anomalies in imaging data as candidates for markers. We pro-<lb>pose AnoGAN, a deep convolutional generative adversarial network to<lb>learn a manifold of normal anatomical variability, accompanying a novel<lb>anomaly scoring scheme based on the mapping from image space to a la-<lb>tent space. Applied to new data, the model labels anomalies, and scores<lb>image patches indicating their fit into the learned distribution. Results<lb>on optical coherence tomography images of the retina demonstrate that<lb>the approach correctly identifies anomalous images, such as images con-<lb>taining retinal fluid or hyperreflective foci.

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery