Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-642-21198-0_70 http://hdl.handle.net/11449/74875 |
Resumo: | This work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer. |
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Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdicamultiscale fractal techniquesmyocardial biopsies imagessymbolic machine learningC4.5 algorithmDiagnosis support systemsLacunarityMultiscale fractalsNumber of clustersPercolation probabilitySymbolic machine learningBiomedical engineeringFractal dimensionLearning systemsSolventsBiopsyThis work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer.Universidade Estadual Paulista DEMAC, Rio ClaroNUTECC Famerp, São José do Rio PretoFaculdade de Medicina de São José Do Rio Preto, São José do Rio PretoInstituto de Anatomia Patológica e Citopatologia, São José do Rio PretoUniversidade de São Paulo FFCLRP Depto. Computação e Matemática, Ribeirão PretoUniversidade Estadual Paulista DEMAC, Rio ClaroUniversidade Estadual Paulista (Unesp)Faculdade de Medicina de São José do Rio Preto (FAMERP)Instituto de Anatomia Patológica e CitopatologiaUniversidade de São Paulo (USP)Carvalho, V. O. [UNESP]Neves, L. A. [UNESP]De Godoy, M. F.Moreira, R. D.Moriel, A. R.Murta, L. O.2014-05-27T11:28:42Z2014-05-27T11:28:42Z2013-03-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject272-275http://dx.doi.org/10.1007/978-3-642-21198-0_705th Latin American Congress on Biomedical Engineering (claib 2011): Sustainable Technologies For the Health of All, Pts 1 and 2. New York: Springer, v. 33, n. 1-2, p. 272-275, 2013.1680-0737http://hdl.handle.net/11449/7487510.1007/978-3-642-21198-0_702-s2.0-848752500241961581092362881Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIFMBE Proceedings0,143info:eu-repo/semantics/openAccess2021-10-23T21:44:13Zoai:repositorio.unesp.br:11449/74875Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:07:19.555658Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
title |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
spellingShingle |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica Carvalho, V. O. [UNESP] multiscale fractal techniques myocardial biopsies images symbolic machine learning C4.5 algorithm Diagnosis support systems Lacunarity Multiscale fractals Number of clusters Percolation probability Symbolic machine learning Biomedical engineering Fractal dimension Learning systems Solvents Biopsy |
title_short |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
title_full |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
title_fullStr |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
title_full_unstemmed |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
title_sort |
Aprendizado de máquina simbólico e técnicas fractais para caracterizar rejeição em biópsia miocárdica |
author |
Carvalho, V. O. [UNESP] |
author_facet |
Carvalho, V. O. [UNESP] Neves, L. A. [UNESP] De Godoy, M. F. Moreira, R. D. Moriel, A. R. Murta, L. O. |
author_role |
author |
author2 |
Neves, L. A. [UNESP] De Godoy, M. F. Moreira, R. D. Moriel, A. R. Murta, L. O. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Faculdade de Medicina de São José do Rio Preto (FAMERP) Instituto de Anatomia Patológica e Citopatologia Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Carvalho, V. O. [UNESP] Neves, L. A. [UNESP] De Godoy, M. F. Moreira, R. D. Moriel, A. R. Murta, L. O. |
dc.subject.por.fl_str_mv |
multiscale fractal techniques myocardial biopsies images symbolic machine learning C4.5 algorithm Diagnosis support systems Lacunarity Multiscale fractals Number of clusters Percolation probability Symbolic machine learning Biomedical engineering Fractal dimension Learning systems Solvents Biopsy |
topic |
multiscale fractal techniques myocardial biopsies images symbolic machine learning C4.5 algorithm Diagnosis support systems Lacunarity Multiscale fractals Number of clusters Percolation probability Symbolic machine learning Biomedical engineering Fractal dimension Learning systems Solvents Biopsy |
description |
This work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol. © 2013 Springer. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-03-26 2014-05-27T11:28:42Z 2014-05-27T11:28:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-642-21198-0_70 5th Latin American Congress on Biomedical Engineering (claib 2011): Sustainable Technologies For the Health of All, Pts 1 and 2. New York: Springer, v. 33, n. 1-2, p. 272-275, 2013. 1680-0737 http://hdl.handle.net/11449/74875 10.1007/978-3-642-21198-0_70 2-s2.0-84875250024 1961581092362881 |
url |
http://dx.doi.org/10.1007/978-3-642-21198-0_70 http://hdl.handle.net/11449/74875 |
identifier_str_mv |
5th Latin American Congress on Biomedical Engineering (claib 2011): Sustainable Technologies For the Health of All, Pts 1 and 2. New York: Springer, v. 33, n. 1-2, p. 272-275, 2013. 1680-0737 10.1007/978-3-642-21198-0_70 2-s2.0-84875250024 1961581092362881 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
IFMBE Proceedings 0,143 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
272-275 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808128318237573120 |