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arXiv · 1810.07430

Learning an MR acquisition-invariant representation using Siamese neural networks

Wouter M. Kouw·Marco Loog·Wilbert Bartels·Adriënne M. Mendrik
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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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Wouter M. Kouw, Marco Loog, Wilbert Bartels, Adriënne M. Mendrik. 2018-10-17. Learning an MR acquisition-invariant representation using Siamese neural networks. https://doi.org/10.1109/isbi.2019.8759281

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