The coronavirus disease (COVID-19) is rapidly spreading all over the world,
and has infected more than 1,436,000 people in more than 200 countries and
territories as of April 9, 2020. Detecting COVID-19 at early stage is essential
to deliver proper healthcare to the patients and also to protect the uninfected
population. To this end, we develop a dual-sampling attention network to
automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in
chest computed tomography (CT). In particular, we propose a novel online
attention module with a 3D convolutional network (CNN) to focus on the
infection regions in lungs when making decisions of diagnoses. Note that there
exists imbalanced distribution of the sizes of the infection regions between
COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom
onset. Therefore, we develop a dual-sampling strategy to mitigate the
imbalanced learning. Our method is evaluated (to our best knowledge) upon the
largest multi-center CT data for COVID-19 from 8 hospitals. In the
training-validation stage, we collect 2186 CT scans from 1588 patients for a
5-fold cross-validation. In the testing stage, we employ another independent
large-scale testing dataset including 2796 CT scans from 2057 patients. Results
show that our algorithm can identify the COVID-19 images with the area under
the receiver operating characteristic curve (AUC) value of 0.944, accuracy of
87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With
this performance, the proposed algorithm could potentially aid radiologists
with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19
outbreak.

该研究提出了一种基于双采样关注网络用于肺部 CT 扫描的 COVID-19 诊断自动化算法，使用了一种新颖的在线注意力模块来诊断 COVID-19 ，并提出了双重采样策略来缓解诊断上的数据不平衡性问题。此算法在 COVID-19 早期诊断方面的性能表现非常棒。