  Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of
International Concern infecting more than 40 million people across 188
countries and territories. Chest computed tomography (CT) imaging technique
benefits from its high diagnostic accuracy and robustness, it has become an
indispensable way for COVID-19 mass testing. Recently, deep learning approaches
have become an effective tool for automatic screening of medical images, and it
is also being considered for COVID-19 diagnosis. However, the high infection
risk involved with COVID-19 leads to relative sparseness of collected labeled
data limiting the performance of such methodologies. Moreover, accurately
labeling CT images require expertise of radiologists making the process
expensive and time-consuming. In order to tackle the above issues, we propose a
supervised domain adaption based COVID-19 CT diagnostic method which can
perform effectively when only a small samples of labeled CT scans are
available. To compensate for the sparseness of labeled data, the proposed
method utilizes a large amount of synthetic COVID-19 CT images and adjusts the
networks from the source domain (synthetic data) to the target domain (real
data) with a cross-domain training mechanism. Experimental results show that
the proposed method achieves state-of-the-art performance on few-shot COVID-19
CT imaging based diagnostic tasks.
