Learning for retinal image quality assessment with label regularization

Background and Objective: The assessment of the image quality is crucial before the computer-aided diagnosis of fundus images. This task is very challenging. Firstly, the subjective judgments of graders on image quality lead to ambiguous labels. Secondly, despite being treated as classification in existing works, grading has regression properties that cannot be ignored. Solving the ambiguity problem and regression problem in the label space, and extracting discriminative features, have become the keys to quality assessment. Methods: In this paper, we proposed a framework that can assess the quality of fundus images accurately and reasonably based on deep convolutional neural networks. Drawing on the experience of human graders, a dual-path convolutional neural network with attention blocks is designed to better extract discriminative features and present the bases of decision. Label smoothing and cost-sensitive regularization are designed to solve the label ambiguity problem and the potential regression problem respectively. Besides, a large number of images are annotated by us to further improve the results. Results: We conducted our experiments on the largest retinal image quality assessment dataset with 28,792 retinal images. Our approach achieves 0.8868 precision, 0.8786 recall, 0.8820 F1, and 0.9138 Kappa score. Results show that our approach outperforms state-of-the-art methods. Conclusions: The promising performances reveal that our methods are beneficial to retinal image quality assessment and have potential in other grading tasks. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a framework for accurately and reasonably assessing the quality of fundus images based on deep convolutional neural networks. By addressing label ambiguity and regression problems, a dual-path convolutional neural network is designed to extract discriminative features and make decisions. Experimental results show that the method performs well on the largest retinal image quality assessment dataset.