Feature selection from large-scale radiomics data: Application to heart disease diagnosis

dc.contributor.author Izquierdo Morcillo, Cristian
dc.date.accessioned 2018-11-05T12:51:32Z
dc.date.available 2018-11-05T12:51:32Z
dc.date.issued 2018-09
dc.description Treball fi de màster de: Master in Intelligent Interactive Systemsca
dc.description Tutors: Karim Lekadir, Irem Cetin
dc.description.abstract Radiomics have become in the past years one of the most interesting fields to be studied and analyzed in medicine. Since their first implementations, researchers have been trying to design new algorithms to develop this tool in order to predict with higher accuracy the appearance of some diseases like cancer. In this thesis we will focus in a method for feature selection, one of the most challenging questions that come up when working with radiomics. The aim of this new algorithm is to improve the accuracy and efficiency by making use of parallel computing. We will face, as well, one of the main issues of the new century, dealing with the Big Data.ca
dc.format.mimetype application/pdf*
dc.identifier.uri http://hdl.handle.net/10230/35698
dc.language.iso engca
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRights info:eu-repo/semantics/openAccessca
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.other Aprenentatge automàtic
dc.subject.other Imatgeria mèdica
dc.title Feature selection from large-scale radiomics data: Application to heart disease diagnosisca
dc.type info:eu-repo/semantics/masterThesisca

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