  The spread of the novel coronavirus disease 2019 (COVID-19) has claimed
millions of lives. Automatic segmentation of lesions from CT images can assist
doctors with screening, treatment, and monitoring. However, accurate
segmentation of lesions from CT images can be very challenging due to data and
model limitations. Recently, Transformer-based networks have attracted a lot of
attention in the area of computer vision, as Transformer outperforms CNN at a
bunch of tasks. In this work, we propose a novel network structure that
combines CNN and Transformer for the segmentation of COVID-19 lesions. We
further propose an efficient semi-supervised learning framework to address the
shortage of labeled data. Extensive experiments showed that our proposed
network outperforms most existing networks and the semi-supervised learning
framework can outperform the base network by 3.0% and 8.2% in terms of Dice
coefficient and sensitivity.
