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.
翻译:1) 现有3DCT分解方法主要依赖2DCT图像,缺乏3D相继制约。 2) 现有的3DCT分解方法侧重于单一比例表示,在3D卷上没有达到多层次可接受字段大小。 3) COVID-19的爆发使得很难说明足够的CT数量来培训深层模型。为了解决这些问题,我们首先建立一个多维注意神经神经神经网络(MDA-CNN),在输入特征图的不同层面综合多尺度信息,并对不同CNN层的多重预测进行监督。第二,我们将MDA-CN作为基本网络,在3D卷上没有达到多层次可接受字段大小。 3) COVID-19的爆发使得很难说明足够的CT数量用于培训深层模型。为了解决这些问题,我们首先建立一个多维关注神经神经网络(MDA-CNN)的多维代神经网络(MDA-CN)的多级共享数据,我们从多层次的CMCD-NA分类数据到连续的多层次的计算数据,我们MDA-NF2的多层次的标签标签数据显示。