@inproceedings{ac3d6e87041541cea5340e44e2f5eea0,
title = "Learning an mr acquisition-invariant representation using siamese neural networks",
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 evaluated on both simulated and real patient data. Experiments show that MRAI-NET outperforms both voxelwise classifiers trained on the source data as well as classifiers trained on the limited amount of target scanner data available.",
keywords = "Acquisition-variation, MRI, Representation learning, Siamese neural network",
author = "Kouw, {W. M.} and M. Loog and Bartels, {L. W.} and Mendrik, {A. M.}",
year = "2019",
month = apr,
day = "1",
doi = "10.1109/ISBI.2019.8759281",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "364--367",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
address = "United States",
note = "16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
}