Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022
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Language
en
Pages
13
76669-76681
76669-76681
Series
IEEE Access, Volume 10
Abstract
Automatic classification of diabetic retinopathy from retinal images has been increasingly studied using deep neural networks with impressive results. However, there is clinical need for estimating uncertainty in the classifications, a shortcoming of modern neural networks. Recently, approximate Bayesian neural networks (BNNs) have been proposed for this task, but previous studies have only considered the binary referable/non-referable diabetic retinopathy classification applied to benchmark datasets. We present novel results for 9 BNNs by systematically investigating a clinical dataset and 5-class classification scheme, together with benchmark datasets and binary classification scheme. Moreover, we derive a connection between entropy-based uncertainty measure and classifier risk, from which we develop a novel uncertainty measure. We observe that the previously proposed entropy-based uncertainty measure improves performance on the clinical dataset for the binary classification scheme, but not to such an extent as on the benchmark datasets. It improves performance in the clinical 5-class classification scheme for the benchmark datasets, but not for the clinical dataset. Our novel uncertainty measure generalizes to the clinical dataset and to one benchmark dataset. Our findings suggest that BNNs can be utilized for uncertainty estimation in classifying diabetic retinopathy on clinical data, though proper uncertainty measures are needed to optimize the desired performance measure. In addition, methods developed for benchmark datasets might not generalize to clinical datasets.Description
Funding Information: The work of Joel Jaskari, Jaakko Sahlsten, and Kimmo K. Kaski was supported in part by the Academy of Finland under Project 345449. Publisher Copyright: © 2013 IEEE.
Keywords
Approximate Bayesian neural networks, deep learning, diabetic retinopathy, reject option classification, uncertainty estimation
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Citation
Jaskari, J, Sahlsten, J, Damoulas, T, Knoblauch, J, Sarkka, S, Karkkainen, L, Hietala, K & Kaski, K K 2022, ' Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification ', IEEE Access, vol. 10, pp. 76669-76681 . https://doi.org/10.1109/ACCESS.2022.3192024