Stochastic variational deep kernel learning based diabetic retinopathy severity grading

Abstract

The retinal disease Diabetic retinopathy (DR) is one of the most probable causes of blindness. Automatic detection of DR is mostly done using convolutional neural networks (CNNs) on colour retinal images. This work in contrast uses stochastic variational deep kernel learning (SVDKL) for DR grading, combining a deep CNN with Gaussian processes (GPs) into a single end-to-end trainable model, which promises to provide predictions with a reliable uncertainty estimate exploiting approximate Bayesian inference. Evaluating the performance and uncertainty calibration of SVDKL on DR grading compared to a plain CNN, the EfficientNet-B0, preliminary results on a subset of the Kaggle DR dataset show a naturally enhanced uncertainty calibration for SVDKL over the plain CNN as well as a good diagnostic performance. Despite SVDKL achieving a slightly reduced accuracy, incorrect predictions were in closer proximity to the target stages, which is beneficial for clinical diagnosis due to minimizing the cost related to severe misclassifications.

Keywords

• deep kernel learning
• uncertainty quantification
• diabetic retinopathy
• medical image processing
• deep learning