Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B., 2020. Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images. In: 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), 4-8 October 2020, Lima, Peru (held online), 665 - 674.
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Official URL: https://doi.org/10.1007/978-3-030-59722-1
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.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | Diabetic Retinopathy Grading; Cost-Sensitive Classifiers |
Group: | Faculty of Science & Technology |
ID Code: | 34882 |
Deposited By: | Symplectic RT2 |
Deposited On: | 25 Nov 2020 15:31 |
Last Modified: | 14 Mar 2022 14:25 |
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