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.
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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.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Syben, C., Stimpel, B., Lommen, J., Würfl, T., Dörfler, A., Maier, A. (2019). Abstract: Fan-to-Parallel Beam Conversion. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_9
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DOI: https://doi.org/10.1007/978-3-658-25326-4_9
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