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

Galdran, A., Dolz, J., Chakor, H., Lombaert, H. and Ayed, I.B., 2020. Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images. Lecture Notes in Computer Science, 12265, 655-674.

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2010.00291v1.pdf - Accepted Version

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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 https://github.com/agaldran/cost_sensitive_loss_classification.

Item Type:Article
ISSN:0302-9743
Additional Information:Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 12265)
Uncontrolled Keywords:Diabetic retinopathy grading Cost-sensitive classifiers
Group:Faculty of Science & Technology
ID Code:35077
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Jan 2021 12:19
Last Modified:15 Aug 2021 08:27

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