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.

背景与目的：电子显微镜（EM）图像中的病毒鉴定被认为是病原体诊断和重新出现传染源的一线方法。但是，现有方法要么专注于检测单个病毒，要么需要大量的手动标记工作来分割病毒。在这项工作中，我们专注于病毒分类的任务，并提出了一种有效且简单的方法来识别不同的病毒。

方法：我们提出了一种残留混合注意力网络（RMAN）进行病毒分类。拟议的网络使用频道注意，自下而上和自上而下的注意，并以端到端的训练方式结合了残留的体系结构，适合处理EM病毒图像并减轻手动注释的负担。

结果：我们通过在透射电子显微镜病毒图像数据集上进行的广泛实验验证了所提出的网络。我们的RMAN在12种病毒类别上的top-1错误率是4.285％，超过了最先进的网络甚至人类专家的错误率。此外，消融研究和类激活映射（CAM）的可视化进一步证明了我们方法的有效性。

结论：所提出的自动化方法有助于医学病毒学的发展，为病毒学家提供了一种识别病毒并协助诊断病毒的高精度方法。