Resampling-based cost loss attention network for explainable imbalanced diabetic retinopathy grading

Abstract

Diabetic retinopathy (DR) is currently considered to be one of the most common diseases that cause blindness. However, DR grading methods are still challenged by the presence of imbalanced class distributions, small lesions, low accuracy of small sample classes and poor explainability. To address these issues, a resampling-based cost loss attention network for explainable imbalanced diabetic retinopathy grading is proposed. First, the progressively-balanced resampling strategy is put forward to create a balanced training data by mixing the two sets of samples obtained from instance-based sampling and class-based sampling. Subsequently, a neuron and normalized channel-spatial attention module (Neu-NCSAM) is designed to learn the global features with 3-D weights and a weight sparsity penalty is applied to the attention module to suppress irrelevant channels or pixels, thereby capturing detailed small lesion information. Thereafter, a weighted loss function of the Cost-Sensitive (CS) regularization and Gaussian label smoothing loss, called cost loss, is proposed to intelligently penalize the incorrect predictions and thus to improve the grading accuracy of small sample classes. Finally, the Gradient-weighted Class Activation Mapping (Grad-CAM) is performed to acquire the localization map of the questionable lesions in order to visually interpret and understand the effect of our model. Comprehensive experiments are carried out on two public datasets, and the subjective and objective results demonstrate that the proposed network outperforms the state-of-the-art methods and achieves the best DR grading results with 83.46%, 60.44%, 65.18%, 63.69% and 92.26% for Kappa, BACC, MCC, F1 and mAUC, respectively.