  The current pandemic, caused by the outbreak of a novel coronavirus
(COVID-19) in December 2019, has led to a global emergency that has
significantly impacted economies, healthcare systems and personal wellbeing all
around the world. Controlling the rapidly evolving disease requires highly
sensitive and specific diagnostics. While real-time RT-PCR is the most commonly
used, these can take up to 8 hours, and require significant effort from
healthcare professionals. As such, there is a critical need for a quick and
automatic diagnostic system. Diagnosis from chest CT images is a promising
direction. However, current studies are limited by the lack of sufficient
training samples, as acquiring annotated CT images is time-consuming. To this
end, we propose a new deep learning algorithm for the automated diagnosis of
COVID-19, which only requires a few samples for training. Specifically, we use
contrastive learning to train an encoder which can capture expressive feature
representations on large and publicly available lung datasets and adopt the
prototypical network for classification. We validate the efficacy of the
proposed model in comparison with other competing methods on two publicly
available and annotated COVID-19 CT datasets. Our results demonstrate the
superior performance of our model for the accurate diagnosis of COVID-19 based
on chest CT images.
