Gated Channel Attention Network for Cataract Classification on AS-OCT Image

Xiao，Zunjie1; Zhang，Xiaoqing1; Higashita，Risa1,2; Hu，Yan1; Yuan，Jin3; Chen，Wan3; Liu，Jiang1,4,5,6

10.1007/978-3-030-92238-2_30

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

13110 LNCS

357-368

Nuclear cataract (NC) is the leading cause of blindness and vision impairment globally. Accurate NC classification is significant for clinical NC diagnosis. Anterior segment optical coherence tomography (AS-OCT) is a non-contact, high-resolution, objective imaging technique, which is widely used in diagnosing ophthalmic diseases. Clinical studies have shown that there is a significant correlation between the pixel density of the lens region on AS-OCT images and NC severity levels; however, automatic NC classification on AS-OCT images has not been seriously studied. Motivated by clinical research, this paper proposes a gated channel attention network (GCA-Net) to classify NC severity levels automatically. In the GCA-Net, we design a gated channel attention block by fusing the clinical priority knowledge, in which a gated layer is designed to filter out abundant features and a Softmax layer is used to build the weakly interacting for channels. We use a clinical AS-OCT image dataset to demonstrate the effectiveness of our GCA-Net. The results showed that the proposed GCA-Net achieves 94.3% in accuracy and outperformed strong baselines and state-of-the-art attention-based networks.

AS-OCT Deep learning Gated channel attention Nuclear cataract

第一 ; 通讯

英语

EI

20220111413432

Computer aided diagnosis ; Deep learning ; Image classification ; Medical imaging ; Optical tomography

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746

2-s2.0-85121922028

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.Tomey Corporation,Nagoya,Japan
3.Zhongshan Ophthalmic Center,Sun Yat-sen University,Guangzhou,China
4.Cixi Institute of Biomedical Engineering,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo,China
5.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
6.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,China

Xiao，Zunjie,Zhang，Xiaoqing,Higashita，Risa,et al. Gated Channel Attention Network for Cataract Classification on AS-OCT Image[C],2021:357-368.