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dc.contributor.authorGonzalez Osorio, Fabio
dc.contributor.authorPerdomo Charry, Oscar Julian
dc.contributor.authorToledo Cortes, Santiago
dc.contributor.authorDe La Pava, Melissa
dc.date.accessioned2021-05-12T19:00:05Z
dc.date.accessioned2021-10-01T17:16:54Z
dc.date.available2021-05-12
dc.date.available2021-10-01T17:16:54Z
dc.date.issued2020
dc.identifier.issn0302-9743
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/1425
dc.description.abstractLa retinopatía diabética (RD) es una de las complicaciones microvasculares de la diabetes mellitus, que sigue siendo una de las principales causas de ceguera en todo el mundo. Los modelos computacionales basados ​​en redes neuronales convolucionales representan el estado del arte para la detección automática de RD utilizando imágenes de fondo de ojo. La mayor parte del trabajo actual aborda este problema como una tarea de clasificación binaria. Sin embargo, incluir la estimación de leyes y la cuantificación de la incertidumbre de las predicciones puede aumentar potencialmente la solidez del modelo. En este artículo, se presenta un método de proceso híbrido de aprendizaje profundo y gaussiano para el diagnóstico de RD y la cuantificación de la incertidumbre. Este método combina el poder de representación del aprendizaje profundo con la capacidad de generalizar a partir de pequeños conjuntos de datos de modelos de procesos gaussianos. Los resultados muestran que la cuantificación de la incertidumbre en las predicciones mejora la interpretabilidad del método como herramienta de apoyo al diagnósticoeng
dc.description.abstractDiabetic retinopathy (DR) is one of the microvascular complications of diabetes mellitus, which remains a leading cause of blindness worldwide. Computational models based on convolutional neural networks represent the state of the art for automatic detection of DR using fundus images. Most of the current work addresses this problem as a binary classification task. However, including law estimation and quantification of prediction uncertainty can potentially increase model robustness. In this paper, a hybrid deep learning and Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning with the ability to generalize from small data sets of Gaussian process models. The results show that the quantification of uncertainty in the predictions improves the interpretability of the method as a diagnostic support tool. Translated with www.DeepL.com/Translator (free version)eng
dc.format.extent10 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherSpringer Sciencespa
dc.sourcehttps://link.springer.com/chapter/10.1007/978-3-030-63419-3_21spa
dc.titleHybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantificationspa
dc.typeArtículo de revistaspa
dc.description.notesEste trabajo fue parcialmente financiado por un premio de investigación de Google y por el proyecto Colciencias número 1101-807-63563.spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.contributor.researchgroupGiBiomespa
dc.identifier.doi10.1007/978-3-030-63419-3_21
dc.identifier.urlDOI:10.1007/978-3-030-63419-3_21
dc.relation.citationeditionLecture Notes in Computer Science (LNCS, volumen 12069)spa
dc.relation.citationissue206spa
dc.relation.citationstartpage215spa
dc.relation.citationvolume12069spa
dc.relation.indexedN/Aspa
dc.relation.ispartofjournalLecture Notes in Computer Sciencespa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.subject.armarcRetinopatía diabética
dc.subject.armarcAprendizaje
dc.subject.proposalDeep Learningspa
dc.subject.proposalDiabetic Retinopathyspa
dc.subject.proposalGaussian Processspa
dc.subject.proposalUncertainty Quantificationspa
dc.subject.proposalAprendizaje profundospa
dc.subject.proposalRetinopatía diabéticaspa
dc.subject.proposalProceso gaussianospa
dc.subject.proposalCuantificación de la incertidumbrespa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa


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