  The capability of generalization to unseen domains is crucial for deep
learning models when considering real-world scenarios. However, current
available medical image datasets, such as those for COVID-19 CT images, have
large variations of infections and domain shift problems. To address this
issue, we propose a prior knowledge driven domain adaptation and a dual-domain
enhanced self-correction learning scheme. Based on the novel learning schemes,
a domain adaptation based self-correction model (DASC-Net) is proposed for
COVID-19 infection segmentation on CT images. DASC-Net consists of a novel
attention and feature domain enhanced domain adaptation model (AFD-DA) to solve
the domain shifts and a self-correction learning process to refine segmentation
results. The innovations in AFD-DA include an image-level activation feature
extractor with attention to lung abnormalities and a multi-level discrimination
module for hierarchical feature domain alignment. The proposed self-correction
learning process adaptively aggregates the learned model and corresponding
pseudo labels for the propagation of aligned source and target domain
information to alleviate the overfitting to noises caused by pseudo labels.
Extensive experiments over three publicly available COVID-19 CT datasets
demonstrate that DASC-Net consistently outperforms state-of-the-art
segmentation, domain shift, and coronavirus infection segmentation methods.
Ablation analysis further shows the effectiveness of the major components in
our model. The DASC-Net enriches the theory of domain adaptation and
self-correction learning in medical imaging and can be generalized to
multi-site COVID-19 infection segmentation on CT images for clinical
deployment.
