Lassila, T. orcid.org/0000-0001-8947-1447, Faria, H.M., Sarrami Foroushani, A. et al. (3 more authors) (2018) Multi-modal synthesis of ASL-MRI features with KPLS regression on heterogeneous data. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. and Fichtinger, G., (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018. 21st International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2018), 16-20 Sep 2018, Granada, Spain. Lecture Notes in Computer Science, 11072 . 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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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Springer Nature Switzerland AG. This is an author produced version of a paper subsequently published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (LNCS 11072). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 VPH DARE - 601055 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jun 2018 12:17 |
Last Modified: | 11 Oct 2018 08:22 |
Published Version: | https://doi.org/10.1007/978-3-030-00931-1_54 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-00931-1_54 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:131501 |