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A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases
dc.contributor.author | Mocioiu, Victor |
dc.contributor.author | de Barros, Nuno M. Pedrosa |
dc.contributor.author | Ortega Martorell, Sandra |
dc.contributor.author | Slotboom, Johannes |
dc.contributor.author | Knecht, Urspeter |
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 | 2016-12-01T10:29:18Z |
dc.date.available | 2016-12-01T10:29:18Z |
dc.date.issued | 2016 |
dc.identifier.citation | Mocioiu, V., de Barros, N., Ortega, S., Slotboom, J., Knecht, U., Arús, C., Vellido, A., Julià, M. A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". Bruges: I6doc.com, 2016, p. 247-252. |
dc.identifier.isbn | 978-287587027-8 |
dc.identifier.uri | http://hdl.handle.net/2117/97584 |
dc.description.abstract | Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | I6doc.com |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.other | Adaptive boosting |
dc.subject.other | Complex networks |
dc.subject.other | Decision trees |
dc.subject.other | Discriminant analysis |
dc.subject.other | Extraction |
dc.subject.other | Face recognition |
dc.subject.other | Factorization |
dc.subject.other | Neural networks |
dc.subject.other | Pathology |
dc.subject.other | Pipelines |
dc.subject.other | Brain metastasis |
dc.subject.other | Knowledge extraction |
dc.subject.other | Linear discriminant analysis |
dc.subject.other | Logistic regressions |
dc.subject.other | Medical domains |
dc.subject.other | Medical practice |
dc.subject.other | Nonnegative matrix factorization |
dc.subject.other | Source extraction |
dc.title | A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-82.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 19287481 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/316679/EU/Transforming Magnetic Resonance Spectroscopy into a Clinical Tool/TRANSACT |
local.citation.author | Mocioiu, V.; de Barros, N.; Ortega, S.; Slotboom, J.; Knecht, U.; Arús, C.; Vellido, A.; Julià, M. |
local.citation.contributor | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
local.citation.pubplace | Bruges |
local.citation.publicationName | ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016 |
local.citation.startingPage | 247 |
local.citation.endingPage | 252 |