To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources ( e.g , time and budget), we propose a Multi-task Multi-slice Deep Learning System (M $^3$ Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e. , slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M $^3$ Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.

中文翻译：

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M$^3$Lung-Sys：用于 CT 成像多类肺肺炎筛查的深度学习系统

为应对新型冠状病毒肺炎（COVID-19）疫情，疑似病例的准确诊断对于及时隔离、医疗救治、防止疫情蔓延发挥着至关重要的作用。考虑到培训案例和资源有限（ 例如 、时间和预算），我们提出了一个多任务多切片深度学习系统（M $^3$ Lung-Sys）用于通过 CT 成像进行多类肺部肺炎筛查，仅由两个 2D CNN 网络组成，IE 、切片和患者级别的分类网络。前者旨在从丰富的CT切片而不是有限的CT体积中寻求特征表示，而对于整体肺炎筛查，后者可以通过不同切片之间的特征细化和聚合来恢复时间信息。除了区分 COVID-19 与健康病例、H1N1 病例和 CAP 病例外，我们的 M $^3$ Lung-Sys 还能够定位相关病变区域，无需任何像素级注释。为了进一步证明我们模型的有效性，我们对总共 734 名患者（251 名健康人、245 名 COVID-19 患者、105 名 H1N1 患者和 133 名 CAP 患者）的胸部 CT 成像数据集进行了广泛的实验。具有大量指标的定量结果表明我们提出的模型在切片和患者级别分类任务上的优越性。更重要的是，生成的病变位置图使我们的系统易于解释，对临床医生更有价值。