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dc.contributor.authorVilamala Muñoz, Albert
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2014-09-12T08:32:13Z
dc.date.available2014-09-12T08:32:13Z
dc.date.created2012
dc.date.issued2012
dc.identifier.citationVilamala, A.; Belanche, Ll.; Vellido, A. Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning. A: International Conference on Neural Networks. "The 2012 International Joint Conference on Neural Networks (IJCNN): Brisbane, Australia (June 10-15, 2012)". Institute of Electrical and Electronics Engineers (IEEE), 2012, p. 1-8.
dc.identifier.isbn978-1-4673-1490-9
dc.identifier.urihttp://hdl.handle.net/2117/24039
dc.description.abstractIn neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due to the localization of the tumour in an extremely sensitive and difficult to reach organ such as the brain. The clinical analysis of brain tumours often requires the use of non-invasive measurement methods, the most common of which resort to imaging techniques. The discrimination between high-grade malignant tumours of different origin but similar characteristics, such as glioblastomas and metastases, is a particularly difficult problem in this context. This is because imaging techniques are often not sensitive enough and their spectroscopic signal is overall too similar. In spite of this, machine learning techniques, coupled with robust feature selection procedures, have recently made substantial inroads into the problem. In this study, magnetic resonance spectroscopy data from an international, multicentre database were used to discriminate between these two types of malignant brain tumours using ensemble learning techniques, with a focus on the definition of a feature selection method specifically designed for ensembles. This method, Breadth Ensemble Learning, takes advantage of the fact that many of the frequencies of the available spectra convey no relevant information for the discrimination of the tumours. The potential of the proposed method is supported by some of the best results reported to date for this problem.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshBrain -- Tumors -- Diagnosis
dc.subject.otherBrain tumours
dc.subject.otherClinical analysis
dc.subject.otherDifferent origins
dc.subject.otherEnsemble learning
dc.subject.otherFeature selection methods
dc.subject.otherGlioblastomas
dc.subject.otherMachine learning techniques
dc.subject.otherMeasurement methods
dc.subject.otherNeuro-oncology
dc.subject.otherRobust feature selection
dc.subject.otherSpectroscopic signals
dc.titleClassifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacCervell -- Tumors
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.1109/IJCNN.2012.6252756
dc.rights.accessOpen Access
local.identifier.drac10911989
dc.description.versionPostprint (author's final draft)
local.citation.authorVilamala, A.; Belanche, Ll.; Vellido, A.
local.citation.contributorInternational Conference on Neural Networks
local.citation.publicationNameThe 2012 International Joint Conference on Neural Networks (IJCNN): Brisbane, Australia (June 10-15, 2012)
local.citation.startingPage1
local.citation.endingPage8


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