  The novel Coronavirus disease (COVID-19) is a highly contagious virus and has
spread all over the world, posing an extremely serious threat to all countries.
Automatic lung infection segmentation from computed tomography (CT) plays an
important role in the quantitative analysis of COVID-19. However, the major
challenge lies in the inadequacy of annotated COVID-19 datasets. Currently,
there are several public non-COVID lung lesion segmentation datasets, providing
the potential for generalizing useful information to the related COVID-19
segmentation task. In this paper, we propose a novel relation-driven
collaborative learning model for annotation-efficient COVID-19 CT lung
infection segmentation. The network consists of encoders with the same
architecture and a shared decoder. The general encoder is adopted to capture
general lung lesion features based on multiple non-COVID lesions, while the
target encoder is adopted to focus on task-specific features of COVID-19
infections. Features extracted from the two parallel encoders are concatenated
for the subsequent decoder part. To thoroughly exploit shared knowledge between
COVID and non-COVID lesions, we develop a collaborative learning scheme to
regularize the relation consistency between extracted features of given input.
Other than existing consistency-based methods that simply enforce the
consistency of individual predictions, our method enforces the consistency of
feature relation among samples, encouraging the model to explore semantic
information from both COVID-19 and non-COVID cases. Extensive experiments on
one public COVID-19 dataset and two public non-COVID datasets show that our
method achieves superior segmentation performance compared with existing
methods in the absence of sufficient high-quality COVID-19 annotations.
