dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
Jo Schlemper, İlkay Öksüz, James R. Clough, Jinming Duan, Andrew Peter King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert · arXiv (Cornell University) · 2019
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.