Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation ( JCS ) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation ( COVID-CS ) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS .

中文翻译：

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JCS：通过联合分类和细分可解释的COVID-19诊断系统

最近，2019年冠状病毒病（COVID-19）在200多个国家/地区引起了大流行性疾病，影响了数十亿人。为了控制感染，识别和分离感染者是最关键的步骤。主要诊断工具是逆转录聚合酶链反应（RT-PCR）测试。尽管如此，RT-PCR测试的灵敏度仍不足以有效预防大流行。胸部CT扫描测试为RT-PCR测试提供了宝贵的补充工具，它可以高灵敏度识别早期患者。但是，胸部CT扫描测试通常很耗时，每例大约需要21.5分钟。本文开发了一种新颖的联合分类和细分（ JCS ）系统执行实时且可解释的COVID-19胸部CT诊断。训练我们JCS 系统，我们构建了大规模的COVID-19分类和细分（ COVID-CS ）数据集，其中包含400例COVID-19患者和350例未感染病例的144,167张胸部CT图像。200例患者的3,855例胸部CT图像带有乳化的细粒度像素级标签，这些标签增加了肺实质的衰减。我们还提供了注释的病灶数，不透明区域和位置，因此有益于各个诊断方面。大量的实验表明，提出的JCS诊断系统对于COVID-19分类和细分非常有效。它在分类测试集上获得95.0％的平均灵敏度和93.0％的特异性，在我们的细分测试集上获得78.5％的Dice得分。COVID-CS数据集。COVID-CS数据集和代码可在以下位置获得：https://github.com/yuhuan-wu/JCS 。