  As the global population continues to face significant negative impact by the
on-going COVID-19 pandemic, there has been an increasing usage of point-of-care
ultrasound (POCUS) imaging as a low-cost and effective imaging modality of
choice in the COVID-19 clinical workflow. A major barrier with widespread
adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert
clinicians that can interpret POCUS examinations, leading to considerable
interest in deep learning-driven clinical decision support systems to tackle
this challenge. A major challenge to building deep neural networks for COVID-19
screening using POCUS is the heterogeneity in the types of probes used to
capture ultrasound images (e.g., convex vs. linear probes), which can lead to
very different visual appearances. In this study, we explore the impact of
leveraging extended linear-convex ultrasound augmentation learning on producing
enhanced deep neural networks for COVID-19 assessment, where we conduct data
augmentation on convex probe data alongside linear probe data that have been
transformed to better resemble convex probe data. Experimental results using an
efficient deep columnar anti-aliased convolutional neural network designed via
a machined-driven design exploration strategy (which we name COVID-Net US-X)
show that the proposed extended linear-convex ultrasound augmentation learning
significantly increases performance, with a gain of 5.1% in test accuracy and
13.6% in AUC.
