Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results. However, there is a clinical need for estimation of the uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian deep learning methods have been proposed for the task but the studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results by systematically investigating a clinical dataset and a clinically relevant 5-class classification scheme, in addition to benchmark datasets and the binary classification scheme. Moreover, we derive a connection between uncertainty measures and classifier risk, from which we develop a new uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure generalizes to the clinical dataset on the binary classification scheme but not on the 5-class scheme, whereas our new uncertainty measure generalizes to the latter case.
@article{arxiv.2201.09042,
title = {Uncertainty-aware deep learning methods for robust diabetic retinopathy classification},
author = {Joel Jaskari and Jaakko Sahlsten and Theodoros Damoulas and Jeremias Knoblauch and Simo Särkkä and Leo Kärkkäinen and Kustaa Hietala and Kimmo Kaski},
journal= {arXiv preprint arXiv:2201.09042},
year = {2022}
}