Automatic lymphocyte detection on gastric cancer IHC images using deep learning

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Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examin...

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Detalles Bibliográficos
Autor: García Ríos, Emilio Rafael
Formato: tesis de maestría
Fecha de Publicación:2017
Institución:Pontificia Universidad Católica del Perú
Repositorio:PUCP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.pucp.edu.pe:20.500.14657/145695
Enlace del recurso:http://hdl.handle.net/20.500.12404/9905
Nivel de acceso:acceso abierto
Materia:Cáncer
Gastroenterología
Aprendizaje automático (Inteligencia artificial)
https://purl.org/pe-repo/ocde/ford#1.02.00
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oai_identifier_str oai:repositorio.pucp.edu.pe:20.500.14657/145695
network_acronym_str RPUC
network_name_str PUCP-Institucional
repository_id_str 2905
spelling Beltrán Castañón, César ArmandoGarcía Ríos, Emilio Rafael2018-01-19T21:32:22Z2018-01-19T21:32:22Z20172018-01-19http://hdl.handle.net/20.500.12404/9905Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.TesisengPontificia Universidad Católica del PerúPEinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/CáncerGastroenterologíaAprendizaje automático (Inteligencia artificial)https://purl.org/pe-repo/ocde/ford#1.02.00Automatic lymphocyte detection on gastric cancer IHC images using deep learninginfo:eu-repo/semantics/masterThesisTesis de maestríareponame:PUCP-Institucionalinstname:Pontificia Universidad Católica del Perúinstacron:PUCPMaestro en Informática con mención en Ciencias de la ComputaciónMaestríaPontificia Universidad Católica del Perú. Escuela de PosgradoInformática con mención en Ciencias de la Computación29561260https://orcid.org/0000-0002-0173-4140611087https://purl.org/pe-repo/renati/level#maestrohttp://purl.org/pe-repo/renati/type#tesis20.500.14657/145695oai:repositorio.pucp.edu.pe:20.500.14657/1456952024-06-10 09:57:39.059http://creativecommons.org/licenses/by-nc-nd/2.5/pe/info:eu-repo/semantics/openAccessmetadata.onlyhttps://repositorio.pucp.edu.peRepositorio Institucional de la PUCPrepositorio@pucp.pe
dc.title.es_ES.fl_str_mv Automatic lymphocyte detection on gastric cancer IHC images using deep learning
title Automatic lymphocyte detection on gastric cancer IHC images using deep learning
spellingShingle Automatic lymphocyte detection on gastric cancer IHC images using deep learning
García Ríos, Emilio Rafael
Cáncer
Gastroenterología
Aprendizaje automático (Inteligencia artificial)
https://purl.org/pe-repo/ocde/ford#1.02.00
title_short Automatic lymphocyte detection on gastric cancer IHC images using deep learning
title_full Automatic lymphocyte detection on gastric cancer IHC images using deep learning
title_fullStr Automatic lymphocyte detection on gastric cancer IHC images using deep learning
title_full_unstemmed Automatic lymphocyte detection on gastric cancer IHC images using deep learning
title_sort Automatic lymphocyte detection on gastric cancer IHC images using deep learning
author García Ríos, Emilio Rafael
author_facet García Ríos, Emilio Rafael
author_role author
dc.contributor.advisor.fl_str_mv Beltrán Castañón, César Armando
dc.contributor.author.fl_str_mv García Ríos, Emilio Rafael
dc.subject.es_ES.fl_str_mv Cáncer
Gastroenterología
Aprendizaje automático (Inteligencia artificial)
topic Cáncer
Gastroenterología
Aprendizaje automático (Inteligencia artificial)
https://purl.org/pe-repo/ocde/ford#1.02.00
dc.subject.ocde.es_ES.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.00
description Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.
publishDate 2017
dc.date.created.es_ES.fl_str_mv 2017
dc.date.accessioned.es_ES.fl_str_mv 2018-01-19T21:32:22Z
dc.date.available.es_ES.fl_str_mv 2018-01-19T21:32:22Z
dc.date.issued.fl_str_mv 2018-01-19
dc.type.es_ES.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.other.none.fl_str_mv Tesis de maestría
format masterThesis
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12404/9905
url http://hdl.handle.net/20.500.12404/9905
dc.language.iso.es_ES.fl_str_mv eng
language eng
dc.rights.es_ES.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/pe/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/2.5/pe/
dc.publisher.es_ES.fl_str_mv Pontificia Universidad Católica del Perú
dc.publisher.country.es_ES.fl_str_mv PE
dc.source.none.fl_str_mv reponame:PUCP-Institucional
instname:Pontificia Universidad Católica del Perú
instacron:PUCP
instname_str Pontificia Universidad Católica del Perú
instacron_str PUCP
institution PUCP
reponame_str PUCP-Institucional
collection PUCP-Institucional
repository.name.fl_str_mv Repositorio Institucional de la PUCP
repository.mail.fl_str_mv repositorio@pucp.pe
_version_ 1835639029693939712
score 13.945474
Automatic lymphocyte detection on gastric cancer IHC images using deep learning
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