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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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 language	English
Title of host publication	ISMRM 27th Annual Meeting & Exhibition (Abstract #658)
Publication status	Published - 24 Sept 2019

Bibliographical note

Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #658)

Keywords

cs.LG
eess.IV
stat.ML

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1909.10995v2

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@inproceedings{90ef9a2a196e43d380272c87e8915662,

title = "dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance",

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. ",

keywords = "cs.LG, eess.IV, stat.ML",

author = "Jo Schlemper and Ilkay Oksuz and Clough, {James R.} and Jinming Duan and King, {Andrew P.} and Schnabel, {Julia A.} and Hajnal, {Joseph V.} and Daniel Rueckert",

note = "Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #658)",

year = "2019",

month = sep,

day = "24",

language = "English",

booktitle = "ISMRM 27th Annual Meeting & Exhibition (Abstract #658)",

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TY - GEN

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T2 - decomposing AUTOMAP to achieve scalability and enhance performance

AU - Schlemper, Jo

AU - Oksuz, Ilkay

AU - Clough, James R.

AU - Duan, Jinming

AU - King, Andrew P.

AU - Schnabel, Julia A.

AU - Hajnal, Joseph V.

AU - Rueckert, Daniel

N1 - Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #658)

PY - 2019/9/24

Y1 - 2019/9/24

N2 - 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.

AB - 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.

KW - cs.LG

KW - eess.IV

KW - stat.ML

M3 - Conference contribution

BT - ISMRM 27th Annual Meeting & Exhibition (Abstract #658)

ER -

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

Abstract

Bibliographical note

Keywords

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