  Radiological image is currently adopted as the visual evidence for COVID-19
diagnosis in clinical. Using deep models to realize automated infection
measurement and COVID-19 diagnosis is important for faster examination based on
radiological imaging. Unfortunately, collecting large training data
systematically in the early stage is difficult. To address this problem, we
explore the feasibility of learning deep models for COVID-19 diagnosis from a
single radiological image by resorting to synthesizing diverse radiological
images. Specifically, we propose a novel conditional generative model, called
CoSinGAN, which can be learned from a single radiological image with a given
condition, i.e., the annotations of the lung and COVID-19 infection. Our
CoSinGAN is able to capture the conditional distribution of visual finds of
COVID-19 infection, and further synthesize diverse and high-resolution
radiological images that match the input conditions precisely. Both deep
classification and segmentation networks trained on synthesized samples from
CoSinGAN achieve notable detection accuracy of COVID-19 infection. Such results
are significantly better than the counterparts trained on the same extremely
small number of real samples (1 or 2 real samples) by using strong data
augmentation, and approximate to the counterparts trained on large dataset
(2846 real images). It confirms our method can significantly reduce the
performance gap between deep models trained on extremely small dataset and on
large dataset, and thus has the potential to realize learning COVID-19
diagnosis from few radiological images in the early stage of COVID-19 pandemic.
Our codes are made publicly available at
https://github.com/PengyiZhang/CoSinGAN.
