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Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
dc.contributor.author | Gonzalez Osorio, Fabio | |
dc.contributor.author | Perdomo Charry, Oscar Julian | |
dc.contributor.author | Toledo Cortes, Santiago | |
dc.contributor.author | De La Pava, Melissa | |
dc.date.accessioned | 2021-05-12T19:00:05Z | |
dc.date.accessioned | 2021-10-01T17:16:54Z | |
dc.date.available | 2021-05-12 | |
dc.date.available | 2021-10-01T17:16:54Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | https://repositorio.escuelaing.edu.co/handle/001/1425 | |
dc.description.abstract | La 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 diagnóstico | eng |
dc.description.abstract | Diabetic 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.extent | 10 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Springer Science | spa |
dc.source | https://link.springer.com/chapter/10.1007/978-3-030-63419-3_21 | spa |
dc.title | Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification | spa |
dc.type | Artículo de revista | spa |
dc.description.notes | Este trabajo fue parcialmente financiado por un premio de investigación de Google y por el proyecto Colciencias número 1101-807-63563. | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.contributor.researchgroup | GiBiome | spa |
dc.identifier.doi | 10.1007/978-3-030-63419-3_21 | |
dc.identifier.url | DOI:10.1007/978-3-030-63419-3_21 | |
dc.relation.citationedition | Lecture Notes in Computer Science (LNCS, volumen 12069) | spa |
dc.relation.citationissue | 206 | spa |
dc.relation.citationstartpage | 215 | spa |
dc.relation.citationvolume | 12069 | spa |
dc.relation.indexed | N/A | spa |
dc.relation.ispartofjournal | Lecture Notes in Computer Science | spa |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.subject.armarc | Retinopatía diabética | |
dc.subject.armarc | Aprendizaje | |
dc.subject.armarc | Método gaussiano | spa |
dc.subject.armarc | Gaussian method | eng |
dc.subject.proposal | Deep Learning | spa |
dc.subject.proposal | Diabetic Retinopathy | spa |
dc.subject.proposal | Gaussian Process | spa |
dc.subject.proposal | Uncertainty Quantification | spa |
dc.subject.proposal | Aprendizaje profundo | spa |
dc.subject.proposal | Retinopatía diabética | spa |
dc.subject.proposal | Proceso gaussiano | spa |
dc.subject.proposal | Cuantificación de la incertidumbre | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
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