Learning an MR acquisition-invariant representation using Siamese neural networks

Conference Paper (2019)
Author(s)

W.M. Kouw (TU Delft - Pattern Recognition and Bioinformatics, University of Copenhagen)

M. Loog (TU Delft - Pattern Recognition and Bioinformatics, University of Copenhagen)

Wilbert Bartels (University Medical Center Utrecht)

A.M. Mendrik (University Medical Center Utrecht, Netherlands eScience Center)

Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.1109/ISBI.2019.8759281
More Info
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Publication Year
2019
Language
English
Research Group
Pattern Recognition and Bioinformatics
Pages (from-to)
364-367
ISBN (print)
978-1-5386-3642-8
ISBN (electronic)
978-1-5386-3641-1

Abstract

Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-net) that extracts acquisition-invariant feature vectors. These can consequently be used by task-specific methods, such as voxelwise classifiers for tissue segmentation. MRAI-net is tested on both simulated and real patient data. Experiments show that MRAI-net outperforms voxelwise classifiers trained on the source or target scanner data when a small number of labeled samples is available.

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