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dc.contributor.authorMocioiu, Victor
dc.contributor.authorKyathanahally, Sreenath P.
dc.contributor.authorArús, Carles
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.authorJulià Sapé, Margarida
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2017-01-17T08:51:09Z
dc.date.available2017-03-25T01:30:33Z
dc.date.issued2016
dc.identifier.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.
dc.identifier.isbn978-3-319-31744-1
dc.identifier.urihttp://hdl.handle.net/2117/99395
dc.description.abstractProton 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.extent9 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshDecision support systems
dc.subject.lcshBrain -- Tumors -- Diagnosis
dc.subject.otherBrain tumors
dc.subject.otherMagnetic resonance spectroscopy
dc.subject.otherConvex non-negative matrix factorization
dc.subject.otherPattern recognition
dc.subject.otherQuality control
dc.subject.otherMachine learning
dc.titleAutomated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization
dc.typeConference report
dc.subject.lemacSistemes d'ajuda a la decisió
dc.subject.lemacCervell -- Tumors -- Diagnòstic
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.1007/978-3-319-31744-1_62
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007%2F978-3-319-31744-1_62
dc.rights.accessOpen Access
local.identifier.drac18770353
dc.description.versionPostprint (author's final draft)
local.citation.authorMocioiu, V.; Kyathanahally, S.; Arús, C.; Vellido, A.; Julià, M.
local.citation.contributorInternational Work-Conference on Bioinformatics and Biomedical Engineering
local.citation.pubplaceGranada
local.citation.publicationNameBioinformatics and Biomedical Engineering: 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016: proceedings
local.citation.startingPage719
local.citation.endingPage727


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