A systematic comparison of generative models for medical images.

Journal: International journal of computer assisted radiology and surgery
Published Date: Feb 7, 2022

Abstract

PURPOSE: This work aims for a systematic comparison of popular shape and appearance models. Here, two statistical and four deep-learning-based shape and appearance models are compared and evaluated in terms of their expressiveness described by their generalization ability and specificity as well as further properties like input data format, interpretability and latent space distribution and dimension.

Authors

  • Hristina Uzunova
    Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. uzunova@imi.uni-luebeck.de.
  • Matthias Wilms
    Department of Radiology, University of Calgary, Calgary, AB, Canada.
  • Nils D Forkert
    Department of Radiology, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca.
  • Heinz Handels
    Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.
  • Jan Ehrhardt
    Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.

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