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Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Authors : Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, Georg Langs

Published in: Information Processing in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.

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previous chapter A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images
next chapter A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases
Footnotes
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Metadata
Title
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Authors
Thomas Schlegl
Philipp Seeböck
Sebastian M. Waldstein
Ursula Schmidt-Erfurth
Georg Langs
Copyright Year
2017
Book
Information Processing in Medical Imaging

Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59050-9_12

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