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Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization
dc.contributor.author | Mocioiu, Victor |
dc.contributor.author | Kyathanahally, Sreenath P. |
dc.contributor.author | Arús, Carles |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.author | Julià Sapé, Margarida |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2017-01-17T08:51:09Z |
dc.date.available | 2017-03-25T01:30:33Z |
dc.date.issued | 2016 |
dc.identifier.citation | Mocioiu, 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. |
dc.identifier.isbn | 978-3-319-31744-1 |
dc.identifier.uri | http://hdl.handle.net/2117/99395 |
dc.description.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. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Decision support systems |
dc.subject.lcsh | Brain -- Tumors -- Diagnosis |
dc.subject.other | Brain tumors |
dc.subject.other | Magnetic resonance spectroscopy |
dc.subject.other | Convex non-negative matrix factorization |
dc.subject.other | Pattern recognition |
dc.subject.other | Quality control |
dc.subject.other | Machine learning |
dc.title | Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization |
dc.type | Conference report |
dc.subject.lemac | Sistemes d'ajuda a la decisió |
dc.subject.lemac | Cervell -- Tumors -- Diagnòstic |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1007/978-3-319-31744-1_62 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://link.springer.com/chapter/10.1007%2F978-3-319-31744-1_62 |
dc.rights.access | Open Access |
local.identifier.drac | 18770353 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Mocioiu, V.; Kyathanahally, S.; Arús, C.; Vellido, A.; Julià, M. |
local.citation.contributor | International Work-Conference on Bioinformatics and Biomedical Engineering |
local.citation.pubplace | Granada |
local.citation.publicationName | Bioinformatics and Biomedical Engineering: 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016: proceedings |
local.citation.startingPage | 719 |
local.citation.endingPage | 727 |