# DRvertGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images

arXiv: https://arxiv.org/abs/1910.11777 · HTML: https://mlsanity.com/paper/1910.11777/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 25 of 25)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision](https://mlsanity.com/paper/2308.07898/) (2023)
- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results](https://mlsanity.com/paper/2112.10074/) (2021)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions](https://mlsanity.com/paper/2406.17536/) (2024)
- [Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation](https://mlsanity.com/paper/2110.14160/) (2021)
- [Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition](https://mlsanity.com/paper/2111.08913/) (2021)
- [Unimodal Distributions for Ordinal Regression](https://mlsanity.com/paper/2303.04547/) (2023)
- [Uncertainty-aware deep learning methods for robust diabetic retinopathy classification](https://mlsanity.com/paper/2201.09042/) (2022)
- [Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models](https://mlsanity.com/paper/2503.02917/) (2025)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning](https://mlsanity.com/paper/2212.00565/) (2022)
- [A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images](https://mlsanity.com/paper/2210.03180/) (2022)
- [A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data](https://mlsanity.com/paper/2109.11806/) (2021)
- [Explainability of Deep Neural Networks for Brain Tumor Detection](https://mlsanity.com/paper/2410.07613/) (2024)
- [Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis](https://mlsanity.com/paper/2602.03622/) (2026)
- [Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2602.21944/) (2026)
- [PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy](https://mlsanity.com/paper/2607.19864/) (2026)

## Abstract (arXiv)

Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patients, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided diagnosis(CAD) systems, but their black-box behaviour hinders the clinical application. We propose DRvertGRADUATE, a novel deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted. We designed DRvertGRADUATE taking into account the ordinal nature of the DR grading problem. A novel Gaussian-sampling approach built upon a Multiple Instance Learning framework allow DRvertGRADUATE to infer an image grade associated with an explanation map and a prediction uncertainty while being trained only with image-wise labels. DRvertGRADUATE was trained on the Kaggle training set and evaluated across multiple datasets. In DR grading, a quadratic-weighted Cohen's kappa (QWK) between 0.71 and 0.84 was achieved in five different datasets. We show that high QWK values occur for images with low prediction uncertainty, thus indicating that this uncertainty is a valid measure of the predictions' quality. Further, bad quality images are generally associated with higher uncertainties, showing that images not suitable for diagnosis indeed lead to less trustworthy predictions. Additionally, tests on unfamiliar medical image data types suggest that DRvertGRADUATE allows outlier detection. The attention maps generally highlight regions of interest for diagnosis. These results show the great potential of DRvertGRADUATE as a second-opinion system in DR severity grading.