Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans

Brain imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in understanding the neurocognitive phenotype and associated challenges of many neurological disorders, providing detailed insights into the structural alterations in the brain. Despite advancements, the lin...

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Detalles Bibliográficos
Autores: Malé, Jordi, Xirau Guardans, Victor, Fortea, Juan, Heuzé, Yann, Martínez-Abadías, Neus, Sevillano, Xavier
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Ramon Llull (URL)
Repositorio:DAU Arxiu Digital de la Universitat Ramon Llull
OAI Identifier:oai:dau.url.edu:20.500.14342/6078
Acceso en línea:http://hdl.handle.net/20.500.14342/6078
https://doi.org/10.3233/FAIA240415
Access Level:acceso abierto
Palabra clave:Unsupervised Deep learning
Autoenders
Brain MRI scans
Anomaly detection
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61
62
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spelling Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI ScansMalé, JordiXirau Guardans, VictorFortea, JuanHeuzé, YannMartínez-Abadías, NeusSevillano, XavierUnsupervised Deep learningAutoendersBrain MRI scansAnomaly detection0046162Brain imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in understanding the neurocognitive phenotype and associated challenges of many neurological disorders, providing detailed insights into the structural alterations in the brain. Despite advancements, the links between cognitive performance and brain anatomy remain unclear. The complexity of analyzing brain MRI scans requires expertise and time, prompting the exploration of artificial intelligence for automated assistance. In this context, unsupervised deep learning techniques, particularly Transformers and Autoencoders, offer a solution by learning the distribution of healthy brain anatomy and detecting alterations in unseen scans. In this work, we evaluate several unsupervised models to reconstruct healthy brain scans and detect synthetic anomalies.info:eu-repo/semantics/publishedVersionIOS PressUniversitat Ramon Llull. La SalleHospital de Sant Pau i la Santa CreuUniversité de BordeauxUniversitat de Barcelona2026202620242024info:eu-repo/semantics/article4 p.application/pdfhttp://hdl.handle.net/20.500.14342/6078https://doi.org/10.3233/FAIA240415reponame:DAU Arxiu Digital de la Universitat Ramon Llullinstname:Universitat Ramon Llull (URL)InglésArtificial Intelligence Research and Development - Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence© L'autor/aAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:dau.url.edu:20.500.14342/60782026-06-21T06:40:37Z
dc.title.none.fl_str_mv Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
title Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
spellingShingle Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
Malé, Jordi
Unsupervised Deep learning
Autoenders
Brain MRI scans
Anomaly detection
004
61
62
title_short Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
title_full Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
title_fullStr Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
title_full_unstemmed Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
title_sort Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
dc.creator.none.fl_str_mv Malé, Jordi
Xirau Guardans, Victor
Fortea, Juan
Heuzé, Yann
Martínez-Abadías, Neus
Sevillano, Xavier
author Malé, Jordi
author_facet Malé, Jordi
Xirau Guardans, Victor
Fortea, Juan
Heuzé, Yann
Martínez-Abadías, Neus
Sevillano, Xavier
author_role author
author2 Xirau Guardans, Victor
Fortea, Juan
Heuzé, Yann
Martínez-Abadías, Neus
Sevillano, Xavier
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. La Salle
Hospital de Sant Pau i la Santa Creu
Université de Bordeaux
Universitat de Barcelona
dc.subject.none.fl_str_mv Unsupervised Deep learning
Autoenders
Brain MRI scans
Anomaly detection
004
61
62
topic Unsupervised Deep learning
Autoenders
Brain MRI scans
Anomaly detection
004
61
62
description Brain imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in understanding the neurocognitive phenotype and associated challenges of many neurological disorders, providing detailed insights into the structural alterations in the brain. Despite advancements, the links between cognitive performance and brain anatomy remain unclear. The complexity of analyzing brain MRI scans requires expertise and time, prompting the exploration of artificial intelligence for automated assistance. In this context, unsupervised deep learning techniques, particularly Transformers and Autoencoders, offer a solution by learning the distribution of healthy brain anatomy and detecting alterations in unseen scans. In this work, we evaluate several unsupervised models to reconstruct healthy brain scans and detect synthetic anomalies.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/6078
https://doi.org/10.3233/FAIA240415
url http://hdl.handle.net/20.500.14342/6078
https://doi.org/10.3233/FAIA240415
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Artificial Intelligence Research and Development - Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence
dc.rights.none.fl_str_mv © L'autor/a
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © L'autor/a
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 4 p.
application/pdf
dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
dc.source.none.fl_str_mv reponame:DAU Arxiu Digital de la Universitat Ramon Llull
instname:Universitat Ramon Llull (URL)
instname_str Universitat Ramon Llull (URL)
reponame_str DAU Arxiu Digital de la Universitat Ramon Llull
collection DAU Arxiu Digital de la Universitat Ramon Llull
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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