  The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world
since its first report in December 2019 and thoracic computed tomography (CT)
has become one of the main tools for its diagnosis. In recent years, deep
learning-based approaches have shown impressive performance in myriad image
recognition tasks. However, they usually require a large number of annotated
data for training. Inspired by Ground Glass Opacity (GGO), a common finding in
COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised
pretraining method based on pseudo lesions generation and restoration for
COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical
model, to generate lesion-like patterns, which were then randomly pasted to the
lung regions of normal CT images to generate pseudo COVID-19 images. The pairs
of normal and pseudo COVID-19 images were then used to train an encoder-decoder
architecture based U-Net for image restoration, which does not require any
labelled data. The pretrained encoder was then fine-tuned using labelled data
for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of
CT images were employed for evaluation. Comprehensive experimental results
demonstrated that the proposed self-supervised learning approach could extract
better feature representation for COVID-19 diagnosis and the accuracy of the
proposed method outperformed the supervised model pretrained on large scale
images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset,
respectively.
