  Automatic medical image segmentation has made great progress benefit from the
development of deep learning. However, most existing methods are based on
convolutional neural networks (CNNs), which fail to build long-range
dependencies and global context connections due to the limitation of receptive
field in convolution operation. Inspired by the success of Transformer in
modeling the long-range contextual information, some researchers have expended
considerable efforts in designing the robust variants of Transformer-based
U-Net. Moreover, the patch division used in vision transformers usually ignores
the pixel-level intrinsic structural features inside each patch. To alleviate
these problems, we propose a novel deep medical image segmentation framework
called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first
attempt to concurrently incorporate the advantages of hierarchical Swin
Transformer into both encoder and decoder of the standard U-shaped architecture
to enhance the semantic segmentation quality of varying medical images. Unlike
many prior Transformer-based solutions, the proposed DS-TransUNet first adopts
dual-scale encoder subnetworks based on Swin Transformer to extract the coarse
and fine-grained feature representations of different semantic scales. As the
core component for our DS-TransUNet, a well-designed Transformer Interactive
Fusion (TIF) module is proposed to effectively establish global dependencies
between features of different scales through the self-attention mechanism.
Furthermore, we also introduce the Swin Transformer block into decoder to
further explore the long-range contextual information during the up-sampling
process. Extensive experiments across four typical tasks for medical image
segmentation demonstrate the effectiveness of DS-TransUNet, and show that our
approach significantly outperforms the state-of-the-art methods.
