  Automated medical image segmentation can assist doctors to diagnose faster
and more accurate. Deep learning based models for medical image segmentation
have made great progress in recent years. However, the existing models fail to
effectively leverage Transformer and MLP for improving U-shaped architecture
efficiently. In addition, the multi-scale features of the MLP have not been
fully extracted in the bottleneck of U-shaped architecture. In this paper, we
propose an efficient U-shaped architecture based on Swin Transformer and
multi-scale MLP, namely STM-UNet. Specifically, the Swin Transformer block is
added to skip connection of STM-UNet in form of residual connection, which can
enhance the modeling ability of global features and long-range dependency.
Meanwhile, a novel PCAS-MLP with parallel convolution module is designed and
placed into the bottleneck of our architecture to contribute to the improvement
of segmentation performance. The experimental results on ISIC 2016 and ISIC
2018 demonstrate the effectiveness of our proposed method. Our method also
outperforms several state-of-the-art methods in terms of IoU and Dice. Our
method has achieved a better trade-off between high segmentation accuracy and
low model complexity.
