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Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders

  • David ZimmererEmail author
  • Fabian Isensee
  • Jens Petersen
  • Simon Kohl
  • Klaus Maier-Hein
Conference paper
  • 50 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious.

Literatur

  1. 1.
    Zimmerer D, Isensee F, Petersen J, et al.; Springer. Unsupervised anomaly localization using variational auto-encoders. Proc MICCAI. 2019;.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • David Zimmerer
    • 1
    Email author
  • Fabian Isensee
    • 1
  • Jens Petersen
    • 1
  • Simon Kohl
    • 1
  • Klaus Maier-Hein
    • 1
  1. 1.German Cancer Research Center (DKFZ)HeidelbergDeutschland

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