{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-anomaly-detection-with","title":"Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery","arxiv_id":"1703.05921","date":"2017-03-17","proceeding":null,"authors":["Thomas Schlegl","Philipp Seeböck","Sebastian M. Waldstein","Ursula Schmidt-Erfurth","Georg Langs"],"abstract":"Obtaining models that capture imaging markers relevant for disease\nprogression and treatment monitoring is challenging. Models are typically based\non large amounts of data with annotated examples of known markers aiming at\nautomating detection. High annotation effort and the limitation to a vocabulary\nof known markers limit the power of such approaches. Here, we perform\nunsupervised learning to identify anomalies in imaging data as candidates for\nmarkers. We propose AnoGAN, a deep convolutional generative adversarial network\nto learn a manifold of normal anatomical variability, accompanying a novel\nanomaly scoring scheme based on the mapping from image space to a latent space.\nApplied to new data, the model labels anomalies, and scores image patches\nindicating their fit into the learned distribution. Results on optical\ncoherence tomography images of the retina demonstrate that the approach\ncorrectly identifies anomalous images, such as images containing retinal fluid\nor hyperreflective foci.","url_abs":"http://arxiv.org/abs/1703.05921v1","url_pdf":"http://arxiv.org/pdf/1703.05921v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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