  The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a
serious threat to public health and the economy. Rapid and accurate diagnosis
of COVID-19 is crucial to prevent the further spread of the disease and reduce
its mortality. Chest Computed tomography (CT) is an effective tool for the
early diagnosis of lung diseases including pneumonia. However, detecting
COVID-19 from CT is demanding and prone to human errors as some early-stage
patients may have negative findings on images. Recently, many deep learning
methods have achieved impressive performance in this regard. Despite their
effectiveness, most of these methods underestimate the rich spatial information
preserved in the 3D structure or suffer from the propagation of errors. To
address this problem, we propose a Dual-Attention Residual Network (DARNet) to
automatically identify COVID-19 from other common pneumonia (CP) and healthy
people using 3D chest CT images. Specifically, we design a dual-attention
module consisting of channel-wise attention and depth-wise attention
mechanisms. The former is utilized to enhance channel independence, while the
latter is developed to recalibrate the depth-level features. Then, we integrate
them in a unified manner to extract and refine the features at different levels
to further improve the diagnostic performance. We evaluate DARNet on a large
public CT dataset and obtain superior performance. Besides, the ablation study
and visualization analysis prove the effectiveness and interpretability of the
proposed method.
