# Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images Source: [Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:37:00.137Z Authors: Adrian Galdran, Jos\\'e Dolz, Hadi Chakor, Herv\\'e Lombaert, Ismail Ben Ayed Published: 2020-10-01T10:42:06.000Z ## Abstract 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/cost\_sensitive\_loss\_classification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-level coronary artery stenosis grading on Private coronary artery stenosis dataset (500 CCTA scans) (test)](): Multi-level coronary artery stenosis grading; Private coronary artery stenosis dataset (500 CCTA scans) (test); QWK: 0.673. - [DR grading on Messidor-2 (test)](): DR Grading; Messidor-2 (test); Quadratic Kappa: 79.79. - [DR grading on Eyepacs (test)](): DR Grading; Eyepacs (test); Quadratic Weighted Kappa: 78.71. ## Original sources and implementation [Original paper]() [PDF]()