Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-319-20309-6_11
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dc.titlePrediction of clinical information from cardiac MRI using manifold learning
dc.contributor.authorWang H.
dc.contributor.authorShi W.
dc.contributor.authorBai W.
dc.contributor.authorde Marvao A.M.S.M.
dc.contributor.authorDawes T.J.
dc.contributor.authorO�Regan D.P.
dc.contributor.authorEdwards P.
dc.contributor.authorCook S.
dc.contributor.authorRueckert D.
dc.date.accessioned2018-11-29T07:15:24Z
dc.date.available2018-11-29T07:15:24Z
dc.date.issued2015
dc.identifier.citationWang H., Shi W., Bai W., de Marvao A.M.S.M., Dawes T.J., O�Regan D.P., Edwards P., Cook S., Rueckert D. (2015). Prediction of clinical information from cardiac MRI using manifold learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9126 : 91-98. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-319-20309-6_11
dc.identifier.issn3029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/149252
dc.description.abstractCardiac MR imaging contains rich information that can be used to investigate the anatomy and function of the heart. In this paper, we demonstrate that it is possible to learn anatomical and functional information from cardiac MR imaging without explicit segmentation in order to predict clinical variables such as blood pressure with high accuracy. To learn the anatomical variations, we build manifolds of different time points across different subjects. In addition, we investigate two different approaches to incorporate motion information into a manifold, and compare these manifolds to a manifold learned from a single time point. Combining both inter- and intra-subject variation, we are able to construct accurate and reliable classifiers to predict clinical variables. Our proposed method does not require any explicit image segmentation and motion estimation and is able to predict clinical variables with good accuracy. � Springer International Publishing Switzerland 2015.
dc.publisherSpringer Verlag
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.doi10.1007/978-3-319-20309-6_11
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume9126
dc.description.page91-98
dc.published.statepublished
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