Melanoma Classification

ABSTRACT : We presented our solution for the SIIM-ISIC melanoma classification challenge. This is a multi-class multi-modal classification model using images and metadata and, we tested both binary and multi-class image-only models and a binary multi-modal model. The keys to success for our solution...

Full description

Autores:
Gómez Giraldo, Oscar Nicolás
Arbeláez López, Néstor Iván
Tipo de recurso:
Tesis
Fecha de publicación:
2021
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/24997
Acceso en línea:
http://hdl.handle.net/10495/24997
Palabra clave:
Análisis de datos
Data analysis
Procesamiento de datos
Data processing
Melanoma
Melanoma
Multi-modal
Data augmentation
http://aims.fao.org/aos/agrovoc/c_4713
http://vocabularies.unesco.org/thesaurus/concept2214
http://vocabularies.unesco.org/thesaurus/concept522
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-sa/2.5/co/
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oai_identifier_str oai:bibliotecadigital.udea.edu.co:10495/24997
network_acronym_str UDEA2
network_name_str Repositorio UdeA
repository_id_str
dc.title.spa.fl_str_mv Melanoma Classification
title Melanoma Classification
spellingShingle Melanoma Classification
Análisis de datos
Data analysis
Procesamiento de datos
Data processing
Melanoma
Melanoma
Multi-modal
Data augmentation
http://aims.fao.org/aos/agrovoc/c_4713
http://vocabularies.unesco.org/thesaurus/concept2214
http://vocabularies.unesco.org/thesaurus/concept522
title_short Melanoma Classification
title_full Melanoma Classification
title_fullStr Melanoma Classification
title_full_unstemmed Melanoma Classification
title_sort Melanoma Classification
dc.creator.fl_str_mv Gómez Giraldo, Oscar Nicolás
Arbeláez López, Néstor Iván
dc.contributor.advisor.none.fl_str_mv Sepúlveda Cano, Lina María
dc.contributor.author.none.fl_str_mv Gómez Giraldo, Oscar Nicolás
Arbeláez López, Néstor Iván
dc.subject.unesco.none.fl_str_mv Análisis de datos
Data analysis
Procesamiento de datos
Data processing
topic Análisis de datos
Data analysis
Procesamiento de datos
Data processing
Melanoma
Melanoma
Multi-modal
Data augmentation
http://aims.fao.org/aos/agrovoc/c_4713
http://vocabularies.unesco.org/thesaurus/concept2214
http://vocabularies.unesco.org/thesaurus/concept522
dc.subject.agrovoc.none.fl_str_mv Melanoma
Melanoma
dc.subject.proposal.spa.fl_str_mv Multi-modal
Data augmentation
dc.subject.agrovocuri.none.fl_str_mv http://aims.fao.org/aos/agrovoc/c_4713
dc.subject.unescouri.none.fl_str_mv http://vocabularies.unesco.org/thesaurus/concept2214
http://vocabularies.unesco.org/thesaurus/concept522
description ABSTRACT : We presented our solution for the SIIM-ISIC melanoma classification challenge. This is a multi-class multi-modal classification model using images and metadata and, we tested both binary and multi-class image-only models and a binary multi-modal model. The keys to success for our solution were the selection of the target variable, using the available metadata, and the data augmentation strategy. Achieving AUC values of 0.95 and F1 of 0.71 for the validation data.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-12-13T18:05:33Z
dc.date.available.none.fl_str_mv 2021-12-13T18:05:33Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv info:eu-repo/semantics/other
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/draft
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_46ec
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/COther
dc.type.local.spa.fl_str_mv Tesis/Trabajo de grado - Monografía - Especialización
format http://purl.org/coar/resource_type/c_46ec
status_str draft
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10495/24997
url http://hdl.handle.net/10495/24997
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.rights.accessrights.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.creativecommons.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/co/
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.extent.spa.fl_str_mv 6
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Medellín
institution Universidad de Antioquia
bitstream.url.fl_str_mv http://bibliotecadigital.udea.edu.co/bitstream/10495/24997/2/license_rdf
http://bibliotecadigital.udea.edu.co/bitstream/10495/24997/4/ArbelaezNestor_2021_MelanomaClassificationModel.pdf
http://bibliotecadigital.udea.edu.co/bitstream/10495/24997/7/license.txt
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bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
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repository.name.fl_str_mv Repositorio Institucional Universidad de Antioquia
repository.mail.fl_str_mv andres.perez@udea.edu.co
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spelling Sepúlveda Cano, Lina MaríaGómez Giraldo, Oscar NicolásArbeláez López, Néstor Iván2021-12-13T18:05:33Z2021-12-13T18:05:33Z2021http://hdl.handle.net/10495/24997ABSTRACT : We presented our solution for the SIIM-ISIC melanoma classification challenge. This is a multi-class multi-modal classification model using images and metadata and, we tested both binary and multi-class image-only models and a binary multi-modal model. The keys to success for our solution were the selection of the target variable, using the available metadata, and the data augmentation strategy. Achieving AUC values of 0.95 and F1 of 0.71 for the validation data.6application/pdfenginfo:eu-repo/semantics/draftinfo:eu-repo/semantics/otherhttp://purl.org/coar/resource_type/c_46echttp://purl.org/redcol/resource_type/COtherTesis/Trabajo de grado - Monografía - Especializaciónhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/co/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-sa/4.0/Melanoma ClassificationMedellínAnálisis de datosData analysisProcesamiento de datosData processingMelanomaMelanomaMulti-modalData augmentationhttp://aims.fao.org/aos/agrovoc/c_4713http://vocabularies.unesco.org/thesaurus/concept2214http://vocabularies.unesco.org/thesaurus/concept522Especialista en Analítica y Ciencia de DatosEspecializaciónFacultad de Ingeniería. Especialización en Analítica y Ciencia de DatosUniversidad de AntioquiaCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81051http://bibliotecadigital.udea.edu.co/bitstream/10495/24997/2/license_rdfe2060682c9c70d4d30c83c51448f4eedMD52ORIGINALArbelaezNestor_2021_MelanomaClassificationModel.pdfArbelaezNestor_2021_MelanomaClassificationModel.pdfapplication/pdf1020771http://bibliotecadigital.udea.edu.co/bitstream/10495/24997/4/ArbelaezNestor_2021_MelanomaClassificationModel.pdf176ad87ae6d2f28392efca158304114eMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/24997/7/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5710495/24997oai:bibliotecadigital.udea.edu.co:10495/249972021-12-13 13:05:49.272Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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