Channel-Wise and Spatial Feature Recalibration Network for Nuclear Cataract Classification

Xiaoqing Zhang1; Gelei Xu1; Junyong Shen1; Zunjie Xiao1; Qiuyang Yan1; Jin Yuan2; Risa Higashita3; Jiang Liu4

10.1109/ICME52920.2022.9860008

2022

1945-7871

978-1-6654-8564-7

2022 IEEE International Conference on Multimedia and Expo (ICME)

2022-July

1-6

18-22 July 2022

Taipei, Taiwan

Nuclear cataract (NC) is a prior age-related disease for blindness and vision impairment globally. Anterior segment optical coherence tomography (AS-OCT) image is a new ophthalmology image, which can capture the lens nucleus region clearly compared with other ophthalmic images, e.g., slit lamp images. Clinical research has suggested that features e.g., mean from AS-OCT images have varying correlations with NC severity levels. However, existing convolutional neural network (CNN) based NC classification works have not incorporated the clinical features into the network design to improve the performance. To this end, we propose a novel channel-wise and spatial feature recalibration network (CSFR-Net) to predict NC severity levels automatically, which is built on a stack of channel-wise and spatial feature recalibration (CSFR) modules. In each CSFR module, we construct a channel-wise feature recalibration block and a spatial feature recalibration block to recalibrate intermediate feature maps dynamically. This feature recalibration strategy enables CSFR-Net to highlight feature representations and suppress unnecessary ones in a global-and-local manner. We conduct extensive experiments on a clinical AS-OCT image dataset and CIFAR benchmarks. The results show that our CSFR-Net achieves better performance than state-of-the-art methods with less model complexity.

Nuclear cataract classification channelwise and spatial feature recalibration attention AS-OCT

第一

EI

20223712732775

Classification (of information) ; Computer vision ; Convolutional neural networks ; Image segmentation ; Lenses ; Medical imaging ; Optical tomography

Biomedical Engineering:461.1 ; Information Theory and Signal Processing:716.1 ; Computer Applications:723.5 ; Vision:741.2 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746 ; Information Sources and Analysis:903.1

IEEE

1.Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhe, China
2.State Key Laboratory of Ophthalmology, Sun Yat-sen University, Guangzhou, China
3.Tomey Corporation, Japan
4.Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China

Xiaoqing Zhang,Gelei Xu,Junyong Shen,et al. Channel-Wise and Spatial Feature Recalibration Network for Nuclear Cataract Classification[C],2022:1-6.