Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83 $\%$ and F1-score of 98.71 $\%$ on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening.

中文翻译：

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使用肺区域先验在胸部 X 射线图像中筛查 COVID-19

COVID-19 的早期筛查对于控制大流行至关重要，从而减轻医疗保健系统的压力。胸部 X 线 (CXR) 的肺分割是一种有前途的肺部疾病早期诊断方法。最近，深度学习在有监督的肺分割方面取得了巨大的成功。然而，由于域转移和缺乏手动像素级注释，如何有效利用肺区域筛查 COVID-19 仍然是一个挑战。我们在此通过使用源自 CXR 图像的肺区域先验提出了一种多外观 COVID-19 筛选框架。首先，我们提出了一个多尺度对抗域适应网络（MS-AdaNet）来提升跨域肺分割任务作为分类网络的先验知识。然后，我们构建了一个多外观网络（MA-Net），它由三个子网络组成，利用肺区域先验实现多外观特征提取和融合。最后，我们可以使用提出的 MA-Net 获得正常、病毒性肺炎和 COVID-19 的预测结果。我们在三个不同的公共 CXR 数据集上扩展了提出的用于肺分割任务的 MS-AdaNet。结果表明，MS-AdaNet 在跨域肺分割中优于对比方法。此外，实验表明，所提出的 MA-Net 的准确率达到了 98.83 结果表明，MS-AdaNet 在跨域肺分割中优于对比方法。此外，实验表明，所提出的 MA-Net 的准确率达到了 98.83 结果表明，MS-AdaNet 在跨域肺分割中优于对比方法。此外，实验表明，所提出的 MA-Net 的准确率达到了 98.83 $\%$F1 分数为 98.71 $\%$关于 COVID-19 筛查。结果表明，所提出的 MA-Net 在 COVID-19 筛查方面可以获得显着的性能。