Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning

Xiao，Zunjie1; Zhang，Xiaoqing1,2; Zheng，Bofang1; Guo，Yitong1; Higashita，Risa1,3; Liu，Jiang1,2,4,5

2024-02-01

10.1016/j.cmpb.2023.107958

Computer Methods and Programs in Biomedicine   影响因子和分区

0169-2607

1872-7565

Background and Objective: Precise cortical cataract (CC) classification plays a significant role in early cataract intervention and surgery. Anterior segment optical coherence tomography (AS-OCT) images have shown excellent potential in cataract diagnosis. However, due to the complex opacity distributions of CC, automatic AS-OCT-based CC classification has been rarely studied. In this paper, we aim to explore the opacity distribution characteristics of CC as clinical priori to enhance the representational capability of deep convolutional neural networks (CNNs) in CC classification tasks. Methods: We propose a novel architectural unit, Multi-style Spatial Attention module (MSSA), which recalibrates intermediate feature maps by exploiting diverse clinical contexts. MSSA first extracts the clinical style context features with Group-wise Style Pooling (GSP), then refines the clinical style context features with Local Transform (LT), and finally executes group-wise feature map recalibration via Style Feature Recalibration (SFR). MSSA can be easily integrated into modern CNNs with negligible overhead. Results: The extensive experiments on a CASIA2 AS-OCT dataset and two public ophthalmic datasets demonstrate the superiority of MSSA over state-of-the-art attention methods. The visualization analysis and ablation study are conducted to improve the explainability of MSSA in the decision-making process. Conclusions: Our proposed MSSANet utilized the opacity distribution characteristics of CC to enhance the representational power and explainability of deep convolutional neural network (CNN) and improve the CC classification performance. Our proposed method has the potential in the early clinical CC diagnosis.

Cortical cataract Explainability Group-wise style pooling Spatial attention

SCI ; EI

英语

第一 ; 通讯

COMPUTER SCIENCE

2-s2.0-85182220827

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
3.TOMEY Corporation,Nagoya,4510051,Japan
4.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China
5.Singapore Eye Research Institute,169856,Singapore

Xiao，Zunjie,Zhang，Xiaoqing,Zheng，Bofang,et al. Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning[J]. Computer Methods and Programs in Biomedicine,2024,244.

Xiao，Zunjie,Zhang，Xiaoqing,Zheng，Bofang,Guo，Yitong,Higashita，Risa,&Liu，Jiang.(2024).Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning.Computer Methods and Programs in Biomedicine,244.

Xiao，Zunjie,et al."Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning".Computer Methods and Programs in Biomedicine 244(2024).