dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert

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Abstract

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.
Original languageUndefined/Unknown
Journalconference abstracts of ISMRM
Publication statusPublished - 24 Sept 2019

Keywords

  • cs.LG
  • eess.IV
  • stat.ML

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