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Using machine learning techniques to explore H-1-MRS data of brain tumors
dc.contributor.author | González Navarro, Félix Fernando |
dc.contributor.author | Belanche Muñoz, Luis Antonio |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2011-07-08T08:56:47Z |
dc.date.available | 2011-07-08T08:56:47Z |
dc.date.created | 2009 |
dc.date.issued | 2009 |
dc.identifier.citation | González, F.; Belanche, Ll. Using machine learning techniques to explore H-1-MRS data of brain tumors. A: Mexican International Conference on Artificial Intelligence. "8th Mexican International Conference on Artificial Intelligence". IEEE Computer Society Publications, 2009, p. 134-139. |
dc.identifier.isbn | 978-0-7695-3933-1 |
dc.identifier.uri | http://hdl.handle.net/2117/12899 |
dc.description.abstract | Machine learning is a powerful paradigm to analyze Proton Magnetic Resonance Spectroscopy (1H-MRS) spectral data for the classification of brain tumor pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this work we apply filter feature selection methods on three types of 1H-MRS spectral data: long echo time, short echo time and an ad hoc combination of both. The experimental findings show that feature selection permits to drastically reduce the dimension, offering at the same time very attractive solutions both in terms of prediction accuracy and the ability to interpret the involved spectral frequencies. A linear dimensionality reduction technique that preserves the class discrimination capabilities is additionally used for visualization of the selected frequencies. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | IEEE Computer Society Publications |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Proton magnetic resonance spectroscopy |
dc.subject.lcsh | Brain -- Tumors |
dc.subject.other | Feature Selection |
dc.subject.other | Classification |
dc.subject.other | Visualization |
dc.title | Using machine learning techniques to explore H-1-MRS data of brain tumors |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Cervell -- Tumors |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1109/MICAI.2009.26 |
dc.relation.publisherversion | https://www.computer.org/csdl/proceedings-article/micai/2009/3933a134/12OmNzIl3Eo |
dc.rights.access | Open Access |
local.identifier.drac | 2517782 |
dc.description.version | Postprint (author’s final draft) |
local.citation.author | González, F.; Belanche, Ll. |
local.citation.contributor | Mexican International Conference on Artificial Intelligence |
local.citation.publicationName | 8th Mexican International Conference on Artificial Intelligence |
local.citation.startingPage | 134 |
local.citation.endingPage | 139 |