Combining heterogeneous data and deep learning models for skin cancer detection

Detalhes bibliográficos
Autor(a) principal: Pacheco, Andre Georghton Cardoso
Data de Publicação: 2020
Tipo de documento: Tese
Idioma: por
Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
Texto Completo: http://repositorio.ufes.br/handle/10/14433
Resumo: Currently, Deep Neural Networks (DNN) are the most successful and common methodologies to tackle medical image analysis. Despite the success, applying Deep Learning for these types of problems involves several challenges such as the lack of large training
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spelling Krohling, Renato Antoniohttps://orcid.org/http://lattes.cnpq.br/5300435085221378Pacheco, Andre Georghton Cardosohttps://orcid.org/http://lattes.cnpq.br/Mota, Vinicius Fernandes Soareshttps://orcid.org/http://lattes.cnpq.br/9305955394665920Cavalieri, Daniel Cruzhttps://orcid.org/http://lattes.cnpq.br/Papa, Joao Paulohttps://orcid.org/http://lattes.cnpq.br/Santos, Celso Alberto Saibelhttps://orcid.org/0000000232875843http://lattes.cnpq.br/76142061641741512024-05-30T00:49:10Z2024-05-30T00:49:10Z2020-11-12Currently, Deep Neural Networks (DNN) are the most successful and common methodologies to tackle medical image analysis. Despite the success, applying Deep Learning for these types of problems involves several challenges such as the lack of large trainingCurrently, Deep Neural Networks (DNN) are the most successful and common methodologies to tackle medical image analysis. Despite the success, applying Deep Learning for these types of problems involves several challenges such as the lack of large trainingFundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Texthttp://repositorio.ufes.br/handle/10/14433porUniversidade Federal do Espírito SantoDoutorado em Ciência da ComputaçãoPrograma de Pós-Graduação em InformáticaUFESBRCentro Tecnológicosubject.br-rjbnCiência da ComputaçãoPalavra-chaveCombining heterogeneous data and deep learning models for skin cancer detectionCombining heterogeneous data and deep learning models for skin cancer detectioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)instname:Universidade Federal do Espírito Santo (UFES)instacron:UFESORIGINALAndreGeorghtonCardosoPacheco-2020-tese.pdfapplication/pdf16277162http://repositorio.ufes.br/bitstreams/23489f67-fdf4-4aa8-8da2-96e63696a7d4/download024129df09a3a24661d238db4c869676MD5110/144332024-07-18 06:00:10.641oai:repositorio.ufes.br:10/14433http://repositorio.ufes.brRepositório InstitucionalPUBhttp://repositorio.ufes.br/oai/requestopendoar:21082024-10-15T18:01:41.474108Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) - Universidade Federal do Espírito Santo (UFES)false
dc.title.none.fl_str_mv Combining heterogeneous data and deep learning models for skin cancer detection
dc.title.alternative.none.fl_str_mv Combining heterogeneous data and deep learning models for skin cancer detection
title Combining heterogeneous data and deep learning models for skin cancer detection
spellingShingle Combining heterogeneous data and deep learning models for skin cancer detection
Pacheco, Andre Georghton Cardoso
Ciência da Computação
Palavra-chave
subject.br-rjbn
title_short Combining heterogeneous data and deep learning models for skin cancer detection
title_full Combining heterogeneous data and deep learning models for skin cancer detection
title_fullStr Combining heterogeneous data and deep learning models for skin cancer detection
title_full_unstemmed Combining heterogeneous data and deep learning models for skin cancer detection
title_sort Combining heterogeneous data and deep learning models for skin cancer detection
author Pacheco, Andre Georghton Cardoso
author_facet Pacheco, Andre Georghton Cardoso
author_role author
dc.contributor.authorID.none.fl_str_mv https://orcid.org/
dc.contributor.authorLattes.none.fl_str_mv http://lattes.cnpq.br/
dc.contributor.advisor1.fl_str_mv Krohling, Renato Antonio
dc.contributor.advisor1ID.fl_str_mv https://orcid.org/
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/5300435085221378
dc.contributor.author.fl_str_mv Pacheco, Andre Georghton Cardoso
dc.contributor.referee1.fl_str_mv Mota, Vinicius Fernandes Soares
dc.contributor.referee1ID.fl_str_mv https://orcid.org/
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/9305955394665920
dc.contributor.referee2.fl_str_mv Cavalieri, Daniel Cruz
dc.contributor.referee2ID.fl_str_mv https://orcid.org/
dc.contributor.referee2Lattes.fl_str_mv http://lattes.cnpq.br/
dc.contributor.referee3.fl_str_mv Papa, Joao Paulo
dc.contributor.referee3ID.fl_str_mv https://orcid.org/
dc.contributor.referee3Lattes.fl_str_mv http://lattes.cnpq.br/
dc.contributor.referee4.fl_str_mv Santos, Celso Alberto Saibel
dc.contributor.referee4ID.fl_str_mv https://orcid.org/0000000232875843
dc.contributor.referee4Lattes.fl_str_mv http://lattes.cnpq.br/7614206164174151
contributor_str_mv Krohling, Renato Antonio
Mota, Vinicius Fernandes Soares
Cavalieri, Daniel Cruz
Papa, Joao Paulo
Santos, Celso Alberto Saibel
dc.subject.cnpq.fl_str_mv Ciência da Computação
topic Ciência da Computação
Palavra-chave
subject.br-rjbn
dc.subject.por.fl_str_mv Palavra-chave
dc.subject.br-rjbn.none.fl_str_mv subject.br-rjbn
description Currently, Deep Neural Networks (DNN) are the most successful and common methodologies to tackle medical image analysis. Despite the success, applying Deep Learning for these types of problems involves several challenges such as the lack of large training
publishDate 2020
dc.date.issued.fl_str_mv 2020-11-12
dc.date.accessioned.fl_str_mv 2024-05-30T00:49:10Z
dc.date.available.fl_str_mv 2024-05-30T00:49:10Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufes.br/handle/10/14433
url http://repositorio.ufes.br/handle/10/14433
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv Text
dc.publisher.none.fl_str_mv Universidade Federal do Espírito Santo
Doutorado em Ciência da Computação
dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Informática
dc.publisher.initials.fl_str_mv UFES
dc.publisher.country.fl_str_mv BR
dc.publisher.department.fl_str_mv Centro Tecnológico
publisher.none.fl_str_mv Universidade Federal do Espírito Santo
Doutorado em Ciência da Computação
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
instname:Universidade Federal do Espírito Santo (UFES)
instacron:UFES
instname_str Universidade Federal do Espírito Santo (UFES)
instacron_str UFES
institution UFES
reponame_str Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
collection Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
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repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) - Universidade Federal do Espírito Santo (UFES)
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