Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly. ¹

中文翻译：

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用于 COVID-19 诊断的增强多中心图卷积网络

2019 冠状病毒 (COVID-19) 疾病的胸部计算机断层扫描 (CT) 扫描通常来自不同医疗中心收集的多个数据集，并且这些图像使用不同的采集协议进行采样。虽然整合多中心数据集增加了样本量，但它会受到中心间异质性的影响。为了解决这个问题，我们提出了一种增强多中心图卷积网络（AM-GCN）来诊断 COVID-19，步骤如下。首先，我们使用 3D 卷积神经网络从初始 CT 扫描中提取特征，其中集成了 Ghost 模块和多任务框架以提高网络的性能。其次，我们利用提取的特征构建多中心图，该图考虑了中心间异质性和训练样本的疾病状态。第三，我们提出了一种增强机制来增强训练样本，从而形成增强的多中心图。最后将增强多中心图输入GCN得到诊断结果。基于来自七个医疗中心的 2223 名 COVID-19 受试者和 2221 名正常对照，我们的方法达到了 97.76% 的平均准确度。我们模型的代码是公开的。 ¹