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dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributor.authorCruz Roa, Angel Alfonso
dc.contributor.authorDíaz Cabrera, Gloria Mercedes
dc.contributor.authorRomero Castro, Eduardo
dc.contributor.authorGonzález Osorio, Fabio Augusto
dc.date.accessioned2019-06-24T17:50:39Z
dc.date.available2019-06-24T17:50:39Z
dc.date.issued2012-01-19
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/9021
dc.description.abstractHistopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively.
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherAssociation for Pathology Informatics
dc.relationhttp://www.jpathinformatics.org/
dc.relation.ispartofUniversidad Nacional de Colombia Sede Bogotá Facultad de Medicina Instituto de Investigaciones Biomédicas
dc.relation.ispartofInstituto de Investigaciones Biomédicas
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc6 Tecnología (ciencias aplicadas) / Technology
dc.subject.ddc61 Ciencias médicas; Medicina / Medicine and health
dc.titleAutomatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization
dc.typeArtículo de revista
dc.type.driverinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.eprintshttp://bdigital.unal.edu.co/5770/
dc.relation.referencesCruz Roa, Angel Alfonso and Díaz Cabrera, Gloria Mercedes and Romero Castro, Eduardo and González Osorio, Fabio Augusto (2012) Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization. Journal of Pathology Informatics, 2 (2). pp. 4-13.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalBasal Cell Carcinoma
dc.subject.proposalHistopathology Images
dc.subject.proposalAutomatic Annotation
dc.subject.proposalVisual Latent Semantic Analysis
dc.subject.proposalNon-negative Matrix Factorization
dc.subject.proposalBag of Features
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit