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Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization

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10.1007/978-3-319-31744-1_62
 
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hdl:2117/99395

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Mocioiu, Victor
Kyathanahally, Sreenath P.
Arús, Carles
Vellido Alcacena, AlfredoMés informacióMés informacióMés informació
Julià Sapé, Margarida
Document typeConference report
Defense date2016
PublisherSpringer
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.
CitationMocioiu, V., Kyathanahally, S., Arús, C., Vellido, A., Julià, M. Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization. A: International Work-Conference on Bioinformatics and Biomedical Engineering. "Bioinformatics and Biomedical Engineering: 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016: proceedings". Granada: Springer, 2016, p. 719-727. 
URIhttp://hdl.handle.net/2117/99395
DOI10.1007/978-3-319-31744-1_62
ISBN978-3-319-31744-1
Publisher versionhttp://link.springer.com/chapter/10.1007%2F978-3-319-31744-1_62
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