Lassila, T orcid.org/0000-0001-8947-1447, Faria, HM, Sarrami-Foroushani, A et al. (3 more authors) (2018) Multi-modal Synthesis of ASL-MRI Features with KPLS Regression on Heterogeneous Data. In: Frangi, AF, Schnabel, JA, Davatzikos,, C, Alberola-López,, C and Fichtinger, G, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018: 21st International Conference on Medical Image Computing and Computer Assisted Intervention, 16-20 Sep 2018, Granada, Spain. Springer , pp. 473-481. ISBN 978-3-030-00930-4
Abstract
Machine learning classifiers are frequently trained on heterogeneous multi-modal imaging data, where some patients have missing modalities. We address the problem of synthesising arterial spin labelling magnetic resonance imaging (ASL-MRI) - derived cerebral blood flow (CBF) - features in a heterogeneous data set. We synthesise ASL-MRI features using T1-weighted structural MRI (sMRI) and carotid ultrasound flow features. To deal with heterogeneous data, we extend the kernel partial least squares regression (kPLSR) - method to the case where both input and output data have partial coverage. The utility of the synthetic CBF features is tested on a binary classification problem of mild cognitive impairment patients vs. controls. Classifiers based on sMRI and synthetic ASL-MRI features are combined using a maximum probability rule, achieving a balanced accuracy of 92% (sensitivity 100 %, specificity 80 %) in a separate validation set. Comparison is made against support vector machine-classifiers from literature.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | © 2018, Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-00931-1_54. Uploaded in accordance with the publisher's self-archiving policy. |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Aug 2018 10:19 |
Last Modified: | 13 Sep 2019 00:38 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-00931-1_54 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134528 |