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dc.contributor.authorMartínez-Murcia, Francisco Jesús
dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorGórriz-Sáez, Juan Manuel
dc.contributor.authorRamírez, Javier
dc.contributor.authorCastillo-Barnes, Diego
dc.date.accessioned2024-01-17T09:52:38Z
dc.date.available2024-01-17T09:52:38Z
dc.date.issued2020-01-01
dc.identifier.citationMartínez-Murcia, Francisco J. et al. “Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.” IEEE Journal of Biomedical and Health Informatics 24 (2020): 17-26.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28806
dc.description.abstractMany classical machine learning techniques have been used to explore Alzheimer’s Disease, evolving from image decomposition techniques such as Principal Component Analysis towards higher-complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of Alzheimer’s Disease (AD) based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegenera- tion process by fusing the information of neuropsychological test outcomes, diagnoses and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analysed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relation.ispartofseriesIEEE Journal of Biomedical and Health Informatics;24(1)
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAlzheimer, Enfermedad dees_ES
dc.subject.otherAlzheimer’s diseasees_ES
dc.subject.otherDeep learninges_ES
dc.subject.otherConvolu- tional autoencoderes_ES
dc.subject.otherManifold learninges_ES
dc.subject.otherData fusiones_ES
dc.titleStudying the Manifold Structure of Alzheimer’s Disease: A Deep Learning Approach Using Convolutional Autoencoderses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1109/JBHI.2019.2914970
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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