论文标题
CCAT-NET：一种基于变压器的新型半监督框架，用于COVID-19
CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation
论文作者
论文摘要
2019年新型冠状病毒病（Covid-19）的传播已夺走了数百万的生命。从CT图像中自动分割病变可以帮助医生进行筛查，治疗和监测。但是，由于数据和模型限制，从CT图像中对病变的准确分割可能非常具有挑战性。最近，基于变压器的网络在计算机视觉领域引起了很多关注，因为变形金刚在一系列任务中优于CNN。在这项工作中，我们提出了一种新型的网络结构，该结构结合了CNN和变压器，用于分割COVID-19。我们进一步提出了一个有效的半监督学习框架，以解决标记数据的短缺。广泛的实验表明，我们提出的网络的表现优于大多数现有网络，而半监督的学习框架可以在骰子系数和敏感性方面胜过3.0％和8.2％的基本网络。
The spread of the novel coronavirus disease 2019 (COVID-19) has claimed millions of lives. Automatic segmentation of lesions from CT images can assist doctors with screening, treatment, and monitoring. However, accurate segmentation of lesions from CT images can be very challenging due to data and model limitations. Recently, Transformer-based networks have attracted a lot of attention in the area of computer vision, as Transformer outperforms CNN at a bunch of tasks. In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions. We further propose an efficient semi-supervised learning framework to address the shortage of labeled data. Extensive experiments showed that our proposed network outperforms most existing networks and the semi-supervised learning framework can outperform the base network by 3.0% and 8.2% in terms of Dice coefficient and sensitivity.
