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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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
Original languageEnglish (US)
Title of host publicationOphthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science, vol 12069
EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
PublisherSpringer
Pages206-215
Number of pages10
ISBN (Electronic)978-3-030-63419-3
ISBN (Print)978-3-030-63418-6
DOIs
StatePublished - Nov 20 2020
Event6th 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, Peru
Duration: Oct 8 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Conference

Conference6th 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
CountryPeru
CityLima
Period10/8/2010/8/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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