  Automated detecting lung infections from computed tomography (CT) data plays
an important role for combating COVID-19. However, there are still some
challenges for developing AI system. 1) Most current COVID-19 infection
segmentation methods mainly relied on 2D CT images, which lack 3D sequential
constraint. 2) Existing 3D CT segmentation methods focus on single-scale
representations, which do not achieve the multiple level receptive field sizes
on 3D volume. 3) The emergent breaking out of COVID-19 makes it hard to
annotate sufficient CT volumes for training deep model. To address these
issues, we first build a multiple dimensional-attention convolutional neural
network (MDA-CNN) to aggregate multi-scale information along different
dimension of input feature maps and impose supervision on multiple predictions
from different CNN layers. Second, we assign this MDA-CNN as a basic network
into a novel dual multi-scale mean teacher network (DM${^2}$T-Net) for
semi-supervised COVID-19 lung infection segmentation on CT volumes by
leveraging unlabeled data and exploring the multi-scale information. Our
DM${^2}$T-Net encourages multiple predictions at different CNN layers from the
student and teacher networks to be consistent for computing a multi-scale
consistency loss on unlabeled data, which is then added to the supervised loss
on the labeled data from multiple predictions of MDA-CNN. Third, we collect two
COVID-19 segmentation datasets to evaluate our method. The experimental results
show that our network consistently outperforms the compared state-of-the-art
methods.
