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Abstract: Fan-to-Parallel Beam Conversion

Deriving Neural Network Architectures Using Precision Learning
  • Christopher SybenEmail author
  • Bernhard Stimpel
  • Jonathan Lommen
  • Tobias Würfl
  • Arnd Dörfler
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

In this paper, we derive a neural network architecture based on an analytical formulation of the parallel-to-fan beam conversion problem following the concept of precision learning [1]. Up to now, this precision learning approach was only used to augment networks with prior knowledge and or to add more flexibility into existing algorithms. We want to extent this approach: we demonstrate that we can drive a mathematical model to tackle a problem under consideration and use deep learning to formulate different hypothesis on efficient solution schemes that are then found as the point of optimality of a deep learning training process.

Literatur

  1. 1.
    Syben C, Stimpel B, Lommen J, et al. Deriving neural network architectures using precision learning: parallel-to-fan beam conversion. Proc GCPR. 2018; p. 1–15.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Christopher Syben
    • 1
    • 2
    Email author
  • Bernhard Stimpel
    • 1
    • 2
  • Jonathan Lommen
    • 1
    • 2
  • Tobias Würfl
    • 1
  • Arnd Dörfler
    • 2
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition Lab, Department of Computer ScienceFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Department of NeuroradiologyUniversitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland

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