# Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images

arXiv: https://arxiv.org/abs/2010.00291 · HTML: https://mlsanity.com/paper/2010.00291/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision](https://mlsanity.com/paper/2308.07898/) (2023)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Full Conformal Adaptation of Medical Vision-Language Models](https://mlsanity.com/paper/2506.06076/) (2025)
- [Performance Metrics for Probabilistic Ordinal Classifiers](https://mlsanity.com/paper/2309.08701/) (2023)
- [Label Smoothing is Robustification against Model Misspecification](https://mlsanity.com/paper/2305.08501/) (2023)
- [From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion](https://mlsanity.com/paper/2409.07757/) (2024)
- [A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data](https://mlsanity.com/paper/2109.11806/) (2021)
- [SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss](https://mlsanity.com/paper/2409.18147/) (2024)
- [Multi-Rater Calibrated Segmentation Models](https://mlsanity.com/paper/2605.02437/) (2026)
- [Clinical Risk-Aware Multi-Level Grading for Coronary Artery Stenosis through Curved Feature Reconstruction](https://mlsanity.com/paper/2606.30082/) (2026)

## Abstract (arXiv)

Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a structure reflects the monotonic relationship between different disease grades. In this paper, we propose a straightforward approach to enforce this constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images based on the well-known notion of Cost-Sensitive classification. We expand standard classification losses with an extra term that acts as a regularizer, imposing greater penalties on predicted grades when they are farther away from the true grade associated to a particular image. Furthermore, we show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading, an approach we refer to as Atomic Sub-Task modeling. This yields models that can implicitly take into account the inherent noise present in DR grade annotations. Our experimental analysis on several public datasets reveals that, when a standard Convolutional Neural Network is trained using this simple strategy, improvements of 3-5% of quadratic-weighted kappa scores can be achieved at a negligible computational cost. Code to reproduce our results is released at https://github.com/agaldran/costsensitivelossclassification.