Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

中文翻译：

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通过结构化潜在多视图表示学习诊断冠状病毒疾病2019（COVID-19）。

最近，2019年冠状病毒病（COVID-19）的爆发在全球范围内迅速蔓延。由于感染患者的人数众多，而且医生的工作量很大，因此迫切需要使用机器学习算法的计算机辅助诊断，并且可以大大减少临床医生的工作量并加快诊断过程。胸部计算机断层扫描（CT）已被认为是诊断疾病的信息工具。在这项研究中，我们建议使用从CT图像中提取的一系列特征来进行COVID-19的诊断。为了充分探索从不同角度描述CT图像的多个特征，学习了一个统一的潜在表示形式，它可以对特征的不同方面的信息进行完全编码，并具有可拆分的有前景的类结构。特别，一组反向神经网络（每种都针对一种类型的特征）保证了完整性，而通过使用类标签，该表示被强制在COVID-19 /社区获得性肺炎（CAP）内紧凑化，并且还有很大的余量。保证在不同类型的肺炎之间。这样，与直接将高维特征投影到类中的情况相比，我们的模型可以很好地避免过度拟合。大量的实验结果表明，所提出的方法优于所有比较方法，并且在改变训练数据数量时可以观察到稳定的性能。而通过使用类标签，该表示法被强制在COVID-19 /社区获得性肺炎（CAP）中紧凑化，并且在不同类型的肺炎之间也保证了较大的余量。这样，与直接将高维特征投影到类中的情况相比，我们的模型可以很好地避免过度拟合。大量的实验结果表明，所提出的方法优于所有比较方法，并且在改变训练数据数量时可以观察到稳定的性能。而通过使用类标签，该表示法被强制在COVID-19 /社区获得性肺炎（CAP）中紧凑化，并且在不同类型的肺炎之间也保证了较大的余量。这样，与直接将高维特征投影到类中的情况相比，我们的模型可以很好地避免过度拟合。大量的实验结果表明，所提出的方法优于所有比较方法，并且在改变训练数据数量时可以观察到稳定的性能。