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.

本文提出了一种基于Swin Transformer和多尺度MLP的高效U形架构，即STM-UNet，用于医学图像分割，旨在有效利用Transformer和MLP以提高模型性能和全局特征建模能力，设计了一种新型PCAS-MLP并将其放置到架构的“瓶颈”中，提高了分割性能。实验结果表明，我们提出的方法在ISIC2016和ISIC2018方面优于其他状态下最先进的方法，同时在高分割准确性和低模型复杂性之间取得了更好的平衡。

基于Swin Transformer和多尺度MLP的高效U形架构用于医学图像分割的STM-UNet