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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

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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 proposeAnoGAN, 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.

T. Schlegl—This work has received funding from IBM, FWF (I2714-B31), OeNB (15356, 15929), the Austrian Federal Ministry of Science, Research and Economy (CDL OPTIMA).

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Authors and Affiliations

  1. Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria

    Thomas Schlegl, Philipp Seeböck & Georg Langs

  2. Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria

    Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein & Ursula Schmidt-Erfurth

Authors
  1. Thomas Schlegl

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  2. Philipp Seeböck

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  3. Sebastian M. Waldstein

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  4. Ursula Schmidt-Erfurth

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  5. Georg Langs

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Corresponding author

Correspondence toThomas Schlegl.

Editor information

Editors and Affiliations

  1. University of North Carolina, Chapel Hill, North Carolina, USA

    Marc Niethammer

  2. University of North Carolina, Chapel Hill, North Carolina, USA

    Martin Styner

  3. Kitware Inc., Carrboro, North Carolina, USA

    Stephen Aylward

  4. University of North Carolina, Chapel Hill, North Carolina, USA

    Hongtu Zhu

  5. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ipek Oguz

  6. University of North Carolina, Chapel Hill, North Carolina, USA

    Pew-Thian Yap

  7. University of North Carolina, Chapel Hill, North Carolina, USA

    Dinggang Shen

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Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G. (2017). Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. In: Niethammer, M.,et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_12

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