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

• URL: http://arxiv.org/abs/2010.00291v1
• Date: Thu, 1 Oct 2020 10:42:06 GMT
• Title: Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
• Authors: Adrian Galdran, Jos\'e Dolz, Hadi Chakor, Herv\'e Lombaert, Ismail Ben Ayed
• Abstract summary: We propose a straightforward approach to enforce the constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images. We expand standard classification losses with an extra term that acts as a regularizer. We show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading.
• Score: 20.480034690570196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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