Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

Santiago Toledo-Cortés, Melissa de la Pava, Oscar Julian Perdomo Charry, Fabio A. González

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaCapítulo (revisado por pares)revisión exhaustiva

1 Cita (Scopus)

Resumen

Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-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 datasets of Gaussian process models
Idioma originalInglés estadounidense
Título de la publicación alojadaOphthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditoresHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas206-215
Número de páginas10
ISBN (versión digital)978-3-030-63419-3
ISBN (versión impresa)978-3-030-63418-6
DOI
EstadoPublicada - nov 20 2020
Evento6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020 - Lima, Perú
Duración: oct 8 2020oct 8 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorialSpringer
Volumen12069 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020
PaísPerú
CiudadLima
Período10/8/2010/8/20

All Science Journal Classification (ASJC) codes

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification'. En conjunto forman una huella única.

Citar esto