CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT

Zhang, Xiaoqing1,2; Xiao, Zunjie2; Hu, Lingxi2; Xu, Gelei2; Higashita, Risa2,3; Chen, Wan4; Yuan, Jin4; Liu, Jiang1,2,5,6

2022-08-17

10.1016/j.knosys.2022.109109

KNOWLEDGE-BASED SYSTEMS   Impact Factor & Quartile

0950-7051

1872-7409

Nuclear cataract (NC) is the leading cause of vision impairment and blindness globally. NC patients can slow the opacity development with early intervention or recover vision with cataract surgery. Anterior segment optical coherence tomography (AS-OCT) images have been increasingly used for clinical NC diagnosis. Compared with other ophthalmic images, e.g., slit lamp images, AS-OCT images are vital for NC diagnosis due to their capability of clearly capturing the nucleus region. Moreover, clinical research has shown the high correlation and repeatability between NC severity levels and image features like mean, maximum, and standard deviation on AS-OCT images. This paper aims to incorporate the clinical features into convolutional neural networks (CNNs) to improve NC classification results and enhance the interpretation of the decision process. Thus, we propose a novel clinical awareness attention network (CCA-Net) to classify NC severity levels automatically. In CCA-Net, we design a practical yet effective clinical-aware attention block, which not only uses the mixed pooling operator to extract clinical features from each channel but also applies the designed clinical integration operator to focus on salient channels. We conduct extensive experiments on one clinical AS-OCT image dataset and two publicly available ophthalmology datasets. The results demonstrate that the CCA-Net outperforms state-of-the-art attention-based CNNs and strong baselines. Moreover, we also provide in-depth analysis to explain the internal behaviors of our method, enhancing the interpretation ability of our method. (C) 2022 Elsevier B.V. All rights reserved.

Nuclear cataract classification Clinical-awareness attention AS-OCT image Interpretation

SCI ; EI

English

First ; Corresponding

Guangdong Provincial Department of Education[2020ZDZX3043] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Shenzhen Natural Science Fund[JCYJ20200109140820699] ; Stable Support Plan Program[20200925174052004]

Computer Science

Computer Science, Artificial Intelligence

WOS:000811334800011

ELSEVIER

20222512240742

Clinical research ; Convolutional neural networks ; Image classification ; Image segmentation ; Optical tomography ; Patient treatment

Medicine and Pharmacology:461.6 ; Data Processing and Image Processing:723.2 ; Optical Devices and Systems:741.3

COMPUTER SCIENCE

Web of Science

1.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
3.Tomey Corp, Nagoya, 4510051, Japan
4.Sun Yat Sen Univ, State Key Lab Ophthalmol, Guangzhou 510060, Peoples R China
5.Cixi Inst Biomed Engn, Ningbo Inst Mat Technol & Engn, Chinese Acad Sci, Ningbo 315201, Peoples R China
6.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain inspired Intelligent, Shenzhen 518055, Peoples R China

Zhang, Xiaoqing,Xiao, Zunjie,Hu, Lingxi,et al. CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT[J]. KNOWLEDGE-BASED SYSTEMS,2022,250.

Zhang, Xiaoqing.,Xiao, Zunjie.,Hu, Lingxi.,Xu, Gelei.,Higashita, Risa.,...&Liu, Jiang.(2022).CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT.KNOWLEDGE-BASED SYSTEMS,250.

Zhang, Xiaoqing,et al."CCA-Net: Clinical-awareness attention network for nuclear cataract classification in AS-OCT".KNOWLEDGE-BASED SYSTEMS 250(2022).