Virus identification in electron microscopy images by residual mixed attention network. (January 2021)

• Record Type: Journal Article
• Authors: Xiao, Chi; Chen, Xi; Xie, Qiwei; Li, Guoqing; Xiao, Hao; Song, Jingdong; Han, Hua
• Is Part Of: Computer methods and programs in biomedicine. Volume 198(2021)
• Publication Date: 2021-01
• Subjects: Virus identification -- viral morphology -- transmission electron microscopy -- deep learning -- attention mechanism; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105766
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We are the first one to incorporate channel attention mechanism with bottom-up and top-down attention to realize the virus identification, and the CAM visualization results illustrate that our method learns well to obtain the attention region of the target virus. On the TEM virus dataset, the top-1 error rate of the proposed method on 12 virus classes is 4.285%, which surpasses that of state-of-the-art networks and even human experts. The fully automated method contributes to the development of medical virology by providing virologists with a high-accuracy approach to recognize viruses and assist in the diagnosis of viruses. 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.