  This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19
diagnostic system. The adaptive visual diagnostics utilize few-shot learning
(FSL) to generate encoded disease state models that are stored and classified
using a dictionary of knowns. The novel vocabulary based feature processing of
the pipeline adapts the knowledge of a pretrained deep neural network to
compress the ultrasound images into discrimative descriptions. The
computational efficiency of the FSL approach enables high diagnostic deep
learning performance in PoC settings, where training data is limited and the
annotation process is not strictly controlled. The algorithm performance is
evaluated on the open source COVID-19 POCUS Dataset to validate the system's
ability to distinguish COVID-19, pneumonia, and healthy disease states. The
results of the empirical analyses demonstrate the appropriate efficiency and
accuracy for scalable PoC use. The code for this work will be made publicly
available on GitHub upon acceptance.
