An efficient deep neural network framework for COVID-19 lung infection segmentation

•A novel network for Coronavirus Disease infected area segmentation from Computed Tomography slices is presented.•The proportion loss is proposed to mitigate the class imbalance, but without losing generality.•A novel semi-supervised framework based on adversarial learning is proposed.•The comprehensive experiments is experimented to demonstrate that the method effectively.