  The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large
number of people worldwide, posing significant stress to the health care
systems. Early and accurate diagnosis of COVID-19 is critical in screening of
infected patients and breaking the person-to-person transmission. Chest X-ray
(CXR) based computer-aided diagnosis of COVID-19 using deep learning becomes a
promising solution to this end. However, the diverse and various radiographic
features of COVID-19 make it challenging, especially when considering each CXR
scan typically only generates one single image. Data scarcity is another issue
since collecting large-scale medical CXR data set could be difficult at
present. Therefore, how to extract more informative and relevant features from
the limited samples available becomes essential. To address these issues,
unlike traditional methods processing each CXR image from a single view, this
paper proposes triple-view convolutional neural networks for COVID-19 diagnosis
with CXR images. Specifically, the proposed networks extract individual
features from three views of each CXR image, i.e., the left lung view, the
right lung view and the overall view, in three streams and then integrate them
for joint diagnosis. The proposed network structure respects the anatomical
structure of human lungs and is well aligned with clinical diagnosis of
COVID-19 in practice. In addition, the labeling of the views does not require
experts' domain knowledge, which is needed by many existing methods. The
experimental results show that the proposed method achieves state-of-the-art
performance, especially in the more challenging three class classification
task, and admits wide generality and high flexibility.
