  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.
