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

• URL: http://arxiv.org/abs/2007.14994v1
• Date: Wed, 29 Jul 2020 04:10:42 GMT
• Title: Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
• Authors: Santiago Toledo-Cort\'es, Melissa De La Pava, Oscar Perd\'omo, and Fabio A. Gonz\'alez
• Abstract summary: Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains one of the leading causes of blindness worldwide. Computational models based on Conal Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The results show that uncertainty quantification in the predictions improves the interpretability of the method as a diagnostic support tool. The source code to replicate the experiments is publicly available at https://github.com/stoledoc/DLGP-DR-Diagnosis.

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