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.
Original language | English |
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Title of host publication | 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) |
Subtitle of host publication | Proceedings |
Place of Publication | Danvers |
Publisher | IEEE |
Pages | 364-367 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-5386-3641-1 |
ISBN (Print) | 978-1-5386-3642-8 |
DOIs | |
Publication status | Published - 2019 |
Event | IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy Duration: 8 Apr 2019 → 11 Apr 2019 https://biomedicalimaging.org/2019/ |
Conference
Conference | IEEE International Symposium on Biomedical Imaging, ISBI 2019 |
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Abbreviated title | ISBI'19 |
Country/Territory | Italy |
City | Venice |
Period | 8/04/19 → 11/04/19 |
Internet address |
Keywords
- MRI
- Acquisition-variation
- Representation-learning
- Siamese-neural-network