  Controlling the COVID-19 pandemic largely hinges upon the existence of fast,
safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or
X-Ray, has many practical advantages and can serve as a globally-applicable
first-line examination technique. We provide the largest publicly available
lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three
classes (COVID-19, bacterial pneumonia, and healthy controls); curated and
approved by medical experts. On this dataset, we perform an in-depth study of
the value of deep learning methods for differential diagnosis of COVID-19. We
propose a frame-based convolutional neural network that correctly classifies
COVID-19 US videos with a sensitivity of 0.98+-0.04 and a specificity of
0.91+-08 (frame-based sensitivity 0.93+-0.05, specificity 0.87+-0.07). We
further employ class activation maps for the spatio-temporal localization of
pulmonary biomarkers, which we subsequently validate for human-in-the-loop
scenarios in a blindfolded study with medical experts. Aiming for scalability
and robustness, we perform ablation studies comparing mobile-friendly, frame-
and video-based architectures and show reliability of the best model by
aleatoric and epistemic uncertainty estimates. We hope to pave the road for a
community effort toward an accessible, efficient and interpretable screening
method and we have started to work on a clinical validation of the proposed
method. Data and code are publicly available.
