CASIA OpenIR  > 类脑智能研究中心  > 微观重建与智能分析
Virus Identification in Electron Microscopy Images by Residual Mixed Attention Network
Xiao, Chi1,2; Chen, Xi2; Xie, Qiwei2,3; Li, Guoqing2; Xiao, Hao4,5; Song, Jingdong4,6; Han, Hua2,7,8
Source PublicationComputer Methods and Programs in Biomedicine
2021
Issue198Pages:105766
Abstract

Background and Objective: Virus identification in electron microscopy (EM) images is considered as one of the front-line method in pathogen diagnosis and re-emerging infectious agents. However, the existing methods either focused on the detection of a single virus or required large amounts of manual labeling work to segment virus. In this work, we focus on the task of virus classification and propose an effective and simple method to identify different viruses.

Methods: We put forward a residual mixed attention network (RMAN) for virus classification. The proposed network uses channel attention, bottom-up and top-down attention, and incorporates a residual architecture in an end-to-end training manner, which is suitable for dealing with EM virus images and reducing the burden of manual annotation.

Results: We validate the proposed network through extensive experiments on a transmission electron microscopy virus image dataset. The top-1 error rate of our RMAN on 12 virus classes is 4.285%, which surpasses that of state-of-the-art networks and even human experts. In addition, the ablation study and the visualization of class activation mapping (CAM) further demonstrate the effectiveness of our method.

Conclusions: The proposed automated method contributes to the development of medical virology, which provides virologists with a high-accuracy approach to recognize viruses as well as assist in the diagnosis of viruses.

KeywordVirus identification viral morphology transmission electron microscopy deep learning attention mechanism
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40682
Collection类脑智能研究中心_微观重建与智能分析
Corresponding AuthorSong, Jingdong; Han, Hua
Affiliation1.School of Biomedical Engineering, Hainan University, Haikou, China
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.Data Mining Lab, Beijing University of Technology, Beijing, China
4.State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
5.College of Physics and Information Science, Key Laboratory of Low-dimensional Quantum Structures, And Quantum Control of the Ministry of Education, Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha, China
6.Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
7.Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
8.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xiao, Chi,Chen, Xi,Xie, Qiwei,et al. Virus Identification in Electron Microscopy Images by Residual Mixed Attention Network[J]. Computer Methods and Programs in Biomedicine,2021(198):105766.
APA Xiao, Chi.,Chen, Xi.,Xie, Qiwei.,Li, Guoqing.,Xiao, Hao.,...&Han, Hua.(2021).Virus Identification in Electron Microscopy Images by Residual Mixed Attention Network.Computer Methods and Programs in Biomedicine(198),105766.
MLA Xiao, Chi,et al."Virus Identification in Electron Microscopy Images by Residual Mixed Attention Network".Computer Methods and Programs in Biomedicine .198(2021):105766.
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