  Recently, deep learning with Convolutional Neural Networks (CNNs) and
Transformers has shown encouraging results in fully supervised medical image
segmentation. However, it is still challenging for them to achieve good
performance with limited annotations for training. In this work, we present a
very simple yet efficient framework for semi-supervised medical image
segmentation by introducing the cross teaching between CNN and Transformer.
Specifically, we simplify the classical deep co-training from consistency
regularization to cross teaching, where the prediction of a network is used as
the pseudo label to supervise the other network directly end-to-end.
Considering the difference in learning paradigm between CNN and Transformer, we
introduce the Cross Teaching between CNN and Transformer rather than just using
CNNs. Experiments on a public benchmark show that our method outperforms eight
existing semi-supervised learning methods just with a simpler framework.
Notably, this work may be the first attempt to combine CNN and transformer for
semi-supervised medical image segmentation and achieve promising results on a
public benchmark. The code will be released at:
https://github.com/HiLab-git/SSL4MIS.
