Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders

Q2

Autores:
Mendoza-Léon, Ricardo
Puentes, John
Uriza Carrasco, Luis Felipe
Hernández Hoyos, Marcela
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Pontificia Universidad Javeriana
Repositorio:
Repositorio Universidad Javeriana
Idioma:
eng
OAI Identifier:
oai:repository.javeriana.edu.co:10554/53823
Acceso en línea:
https://www.sciencedirect.com/science/article/pii/S0010482519303865?via%3Dihub
http://hdl.handle.net/10554/53823
https://doi.org/10.1016/j.compbiomed.2019.103527
Palabra clave:
Alzheimer disease
Supervised autoencoder
Supervised switching autoencoder
Convolutional neural networks
Representation learning
Magnetic resonance imaging
Rights
License
Atribución-NoComercial 4.0 Internacional
id JAVERIANA2_9a8852e3631dde3bd6e82ed613a14133
oai_identifier_str oai:repository.javeriana.edu.co:10554/53823
network_acronym_str JAVERIANA2
network_name_str Repositorio Universidad Javeriana
repository_id_str
dc.title.spa.fl_str_mv Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
title Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
spellingShingle Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
Alzheimer disease
Supervised autoencoder
Supervised switching autoencoder
Convolutional neural networks
Representation learning
Magnetic resonance imaging
title_short Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
title_full Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
title_fullStr Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
title_full_unstemmed Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
title_sort Single-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders
dc.creator.fl_str_mv Mendoza-Léon, Ricardo
Puentes, John
Uriza Carrasco, Luis Felipe
Hernández Hoyos, Marcela
dc.contributor.author.none.fl_str_mv Mendoza-Léon, Ricardo
Puentes, John
Uriza Carrasco, Luis Felipe
Hernández Hoyos, Marcela
dc.contributor.corporatename.none.fl_str_mv Pontificia Universidad Javeriana. Facultad de Medicina. Departamento de Radiología e Imágenes Diagnósticas
Pontificia Universidad Javeriana. Facultad de Medicina. Hospital Universitario San Ignacio
dc.subject.keyword.spa.fl_str_mv Alzheimer disease
Supervised autoencoder
Supervised switching autoencoder
Convolutional neural networks
Representation learning
Magnetic resonance imaging
topic Alzheimer disease
Supervised autoencoder
Supervised switching autoencoder
Convolutional neural networks
Representation learning
Magnetic resonance imaging
description Q2
publishDate 2020
dc.date.created.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-05-10T22:26:35Z
dc.date.available.none.fl_str_mv 2021-05-10T22:26:35Z
dc.type.local.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
format http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.spa.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0010482519303865?via%3Dihub
dc.identifier.issn.spa.fl_str_mv 0010-4825 /1879-0534
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10554/53823
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.compbiomed.2019.103527
dc.identifier.instname.spa.fl_str_mv instname:Pontificia Universidad Javeriana
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional - Pontificia Universidad Javeriana
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.javeriana.edu.co
url https://www.sciencedirect.com/science/article/pii/S0010482519303865?via%3Dihub
http://hdl.handle.net/10554/53823
https://doi.org/10.1016/j.compbiomed.2019.103527
identifier_str_mv 0010-4825 /1879-0534
instname:Pontificia Universidad Javeriana
reponame:Repositorio Institucional - Pontificia Universidad Javeriana
repourl:https://repository.javeriana.edu.co
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationendpage.spa.fl_str_mv 14
dc.relation.ispartofjournal.spa.fl_str_mv Computers in Biology and Medicine
dc.relation.citationvolume.spa.fl_str_mv 116
dc.rights.licence.*.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
dc.format.spa.fl_str_mv PDF
dc.format.mimetype.spa.fl_str_mv application/pdf
institution Pontificia Universidad Javeriana
bitstream.url.fl_str_mv http://repository.javeriana.edu.co/bitstream/10554/53823/1/Single-slice%20Alzheimer%27s%20disease%20classification%20and%20disease%20regional%20analysis%20with%20supervised%20switching%20autoencoders.pdf
http://repository.javeriana.edu.co/bitstream/10554/53823/2/license.txt
http://repository.javeriana.edu.co/bitstream/10554/53823/3/Single-slice%20Alzheimer%27s%20disease%20classification%20and%20disease%20regional%20analysis%20with%20supervised%20switching%20autoencoders.pdf.jpg
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2e69d0a80e1b9e4b7aec9ae6e1887915
bitstream.checksumAlgorithm.fl_str_mv MD5
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repository.name.fl_str_mv Repositorio Institucional - Pontificia Universidad Javeriana
repository.mail.fl_str_mv repositorio@javeriana.edu.co
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spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/http://purl.org/coar/access_right/c_abf2Mendoza-Léon, RicardoPuentes, JohnUriza Carrasco, Luis FelipeHernández Hoyos, MarcelaPontificia Universidad Javeriana. Facultad de Medicina. Departamento de Radiología e Imágenes DiagnósticasPontificia Universidad Javeriana. Facultad de Medicina. Hospital Universitario San Ignacio2021-05-10T22:26:35Z2021-05-10T22:26:35Z2020https://www.sciencedirect.com/science/article/pii/S0010482519303865?via%3Dihub0010-4825 /1879-0534http://hdl.handle.net/10554/53823https://doi.org/10.1016/j.compbiomed.2019.103527instname:Pontificia Universidad Javerianareponame:Repositorio Institucional - Pontificia Universidad Javerianarepourl:https://repository.javeriana.edu.coPDFapplication/pdfengSingle-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencodersArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Q2Q2Background Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this article, we explore the application of Supervised Switching Autoencoders (SSAs) to perform AD classification using only one structural Magnetic Resonance Imaging (sMRI) slice. SSAs are revised supervised autoencoder architectures, combining unsupervised representation and supervised classification as one unified model. In this work, we study the capabilities of SSAs to capture complex visual neurodegeneration patterns, and fuse disease semantics simultaneously. We also examine how regions associated to disease state can be discovered by SSAs following a local patch-based approach. Results Our experiments employing a single 2D T1-w sMRI slice per subject show that SSAs perform similarly to previous proposals that rely on full volumetric information and feature-engineered representations. SSAs classification accuracy on slices extracted along the Axial, Coronal, and Sagittal anatomical planes from a balanced cohort of 40 independent test subjects was 87.5%, 90.0%, and 90.0%, respectively. A top sensitivity of 95.0% on both Coronal and Sagittal planes was also obtained. Conclusions SSAs provided well-ranked accuracy performance among previous classification proposals, including feature-engineered and feature learning based methods, using only one scan slice per subject, instead of the whole 3D volume, as it is conventionally done. In addition, regions identified as relevant by SSAs’ were, in most part, coherent or partially coherent in regard to relevant regions reported on previous works. These regions were also associated with findings from medical knowledge, which gives value to our methodology as a potential analytical aid for disease understanding.Revista Internacional - IndexadaAlzheimer diseaseSupervised autoencoderSupervised switching autoencoderConvolutional neural networksRepresentation learningMagnetic resonance imaging114Computers in Biology and Medicine116ORIGINALSingle-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders.pdfSingle-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders.pdfArtículoapplication/pdf1777826http://repository.javeriana.edu.co/bitstream/10554/53823/1/Single-slice%20Alzheimer%27s%20disease%20classification%20and%20disease%20regional%20analysis%20with%20supervised%20switching%20autoencoders.pdfbb95e82713415d4c30d7b7432646dc01MD51embargoed access|||9999-05-09LICENSElicense.txtlicense.txttext/plain; charset=utf-82603http://repository.javeriana.edu.co/bitstream/10554/53823/2/license.txt2070d280cc89439d983d9eee1b17df53MD52open accessTHUMBNAILSingle-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders.pdf.jpgSingle-slice Alzheimer's disease classification and disease regional analysis with supervised switching autoencoders.pdf.jpgIM Thumbnailimage/jpeg9675http://repository.javeriana.edu.co/bitstream/10554/53823/3/Single-slice%20Alzheimer%27s%20disease%20classification%20and%20disease%20regional%20analysis%20with%20supervised%20switching%20autoencoders.pdf.jpg2e69d0a80e1b9e4b7aec9ae6e1887915MD53open access10554/53823oai:repository.javeriana.edu.co:10554/538232023-03-21 10:46:51.549Repositorio Institucional - Pontificia Universidad Javerianarepositorio@javeriana.edu.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