# Uncertainty-aware deep learning methods for robust diabetic retinopathy classification

arXiv: https://arxiv.org/abs/2201.09042 · HTML: https://mlsanity.com/paper/2201.09042/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks](https://mlsanity.com/paper/1912.10481/) (2019)
- [Generalized Variational Inference: Three arguments for deriving new Posteriors](https://mlsanity.com/paper/1904.02063/) (2019)
- [Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy](https://mlsanity.com/paper/1710.01711/) (2017)
- [Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning](https://mlsanity.com/paper/1907.00865/) (2019)
- [DRvertGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images](https://mlsanity.com/paper/1910.11777/) (2019)
- [Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading](https://mlsanity.com/paper/1904.08764/) (2019)

## Built on by (most cited first, 8 of 8)

- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Dynamic Uncertainty Learning with Noisy Correspondence for Text-Based Person Search](https://mlsanity.com/paper/2505.06566/) (2025)
- [Uncertainty-Aware Ordinal Deep Learning for cross-Dataset Diabetic Retinopathy Grading](https://mlsanity.com/paper/2602.10315/) (2026)
- [UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection](https://mlsanity.com/paper/2211.03148/) (2022)
- [UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts](https://mlsanity.com/paper/2304.04539/) (2023)
- [On the meaning of uncertainty for ethical AI: philosophy and practice](https://mlsanity.com/paper/2309.05529/) (2023)
- [Enhancing Safety in Diabetic Retinopathy Detection: Uncertainty-Aware Deep Learning Models with Rejection Capabilities](https://mlsanity.com/paper/2510.00029/) (2025)
- [Multi-Channel Feature Fusion and Monte Carlo Dropout for Uncertainty-Aware Diabetic Retinopathy Grading](https://mlsanity.com/paper/2608.15234/) (2026)

## Abstract (arXiv)

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.