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

Authors: Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ben Ayed, I.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 12265 LNCS

Pages: 665-674

eISSN: 1611-3349

ISSN: 0302-9743

DOI: 10.1007/978-3-030-59722-1_64

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 github.com/agaldran/cost_sensitive_loss_classification.

http://eprints.bournemouth.ac.uk/35077/

Source: Scopus

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

Authors: Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B.

Journal: CoRR

Volume: abs/2010.00291

http://eprints.bournemouth.ac.uk/35077/

Source: DBLP

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

Authors: Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B.

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.

http://eprints.bournemouth.ac.uk/35077/

Source: arXiv