  Accurate medical image segmentation is critical for disease quantification
and treatment evaluation. While traditional Unet architectures and their
transformer-integrated variants excel in automated segmentation tasks. However,
they lack the ability to harness the intrinsic position and channel features of
image. Existing models also struggle with parameter efficiency and
computational complexity, often due to the extensive use of Transformers. To
address these issues, this study proposes a novel deep medical image
segmentation framework, called DA-TransUNet, aiming to integrate the
Transformer and dual attention block(DA-Block) into the traditional U-shaped
architecture. Unlike earlier transformer-based U-net models, DA-TransUNet
utilizes Transformers and DA-Block to integrate not only global and local
features, but also image-specific positional and channel features, improving
the performance of medical image segmentation. By incorporating a DA-Block at
the embedding layer and within each skip connection layer, we substantially
enhance feature extraction capabilities and improve the efficiency of the
encoder-decoder structure. DA-TransUNet demonstrates superior performance in
medical image segmentation tasks, consistently outperforming state-of-the-art
techniques across multiple datasets. In summary, DA-TransUNet offers a
significant advancement in medical image segmentation, providing an effective
and powerful alternative to existing techniques. Our architecture stands out
for its ability to improve segmentation accuracy, thereby advancing the field
of automated medical image diagnostics. The codes and parameters of our model
will be publicly available at https://github.com/SUN-1024/DA-TransUnet.
