Learning for retinal image quality assessment with label regularization

Tianjiao Guo; Ziyun Liang; Yun Gu; Kun Liu; Xun Xu; Jie Yang; Qi Yu

doi:10.1016/j.cmpb.2022.107238

Learning for retinal image quality assessment with label regularization

Comput Methods Programs Biomed. 2023 Jan:228:107238. doi: 10.1016/j.cmpb.2022.107238. Epub 2022 Nov 13.

Authors

Tianjiao Guo¹, Ziyun Liang², Yun Gu², Kun Liu³, Xun Xu³, Jie Yang⁴, Qi Yu⁵

Affiliations

¹ Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong Univeristy, Shanghai, China. Electronic address: 292725651@sjtu.edu.cn.
² Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine,Shanghai, China; National Clinical Research Center for Eye Diseases, Shanghai, China.
⁴ Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. Electronic address: jieyang@sjtu.edu.cn.
⁵ Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine,Shanghai, China; National Clinical Research Center for Eye Diseases, Shanghai, China. Electronic address: yu.qi@sjtu.edu.cn.

PMID: 36423485
DOI: 10.1016/j.cmpb.2022.107238

Abstract

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.

Keywords: Convolutional neural network (CNN); Cost-sensitive; Label smoothing; Quality; Retinal image.