Automatic cardiac segmentation by fusing deep learning models

dc.contributor.author Rodríguez Prado, Diego Vincent
dc.date.accessioned 2019-10-04T10:54:58Z
dc.date.available 2019-10-04T10:54:58Z
dc.date.issued 2019
dc.description Treball de fi de grau en Sistemes Audiovisualsca
dc.description Tutor: Karim Lekadir
dc.description.abstract Nowadays machine learning models can be used to automate the process of cardiac segmentation, a tedious task usually done by cardiologists and radiologists to diagnose heart diseases and get insights of a certain patient’s heart. In this work, we propose combining state-of-the-art deep learning based models to automatically delineate cardiac MRI slices. By combining existing successful models—using both a stacking ensemble and a majority voting algorithm—we get similar or better results than the existing individual methods. In the experiments carried out, the ensemble methods outperform the original baseline models.ca
dc.format.mimetype application/pdf*
dc.identifier.uri http://hdl.handle.net/10230/42385
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 Imatges tridimensionals en biologia
dc.subject.other Sistema cardiovascular -- Malalties
dc.subject.other Aprenentatge automàtic
dc.title Automatic cardiac segmentation by fusing deep learning modelsca
dc.type info:eu-repo/semantics/bachelorThesisca

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