  In this paper, we introduce a novel semi-supervised learning framework
tailored for medical image segmentation. Central to our approach is the
innovative Multi-scale Text-aware ViT-CNN Fusion scheme. This scheme adeptly
combines the strengths of both ViTs and CNNs, capitalizing on the unique
advantages of both architectures as well as the complementary information in
vision-language modalities. Further enriching our framework, we propose the
Multi-Axis Consistency framework for generating robust pseudo labels, thereby
enhancing the semisupervised learning process. Our extensive experiments on
several widelyused datasets unequivocally demonstrate the efficacy of our
approach.
