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As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspectsof life and the global healthcare systems, the adoption of rapid and effectivescreening methods to prevent further spread of the virus and lessen the burdenon healthcare providers is a necessity. As a cheap and widely accessiblemedical image modality, point-of-care ultrasound (POCUS) imaging allowsradiologists to identify symptoms and assess severity through visual inspectionof the chest ultrasound images. Combined with the recent advancements incomputer science, applications of deep learning techniques in medical imageanalysis have shown promising results, demonstrating that artificialintelligence-based solutions can accelerate the diagnosis of COVID-19 and lowerthe burden on healthcare professionals. However, the lack of a huge amount ofwell-annotated data poses a challenge in building effective deep neuralnetworks in the case of novel diseases and pandemics. Motivated by this, wepresent COVID-Net USPro, an explainable few-shot deep prototypical network,that monitors and detects COVID-19 positive cases with high precision andrecall from minimal ultrasound images. COVID-Net USPro achieves 99.65% overallaccuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases whentrained with only 5 shots. The analytic pipeline and results were verified byour contributing clinician with extensive experience in POCUS interpretation,ensuring that the network makes decisions based on actual patterns.

COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical  Network to Monitor and Detect COVID-19 Infection from Point-of-Care  Ultrasound Images