  This paper presents a fully-automated method for the identification of
suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One
major role of chest CT scanning in COVID-19 diagnoses is identification of an
inflammation particular to the disease. This task is generally performed by
radiologists through an interpretation of the CT volumes, however, because of
the heavy workload, an automatic analysis method using a computer is desired.
Most computer-aided diagnosis studies have addressed only a portion of the
elements necessary for the identification. In this work, we realize the
identification method through a classification task by using a 2.5-dimensional
CNN with three-dimensional attention mechanisms. We visualize the suspicious
regions by applying a backpropagation based on positive gradients to
attention-weighted features. We perform experiments on an in-house dataset and
two public datasets to reveal the generalization ability of the proposed
method. The proposed architecture achieved AUCs of over 0.900 for all the
datasets, and mean sensitivity $0.853 \pm 0.036$ and specificity $0.870 \pm
0.040$. The method can also identify notable lesions pointed out in the
radiology report as suspicious regions.
