MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image
  Segmentation
Jing Xu
Although transformer is preferred in natural language processing, few studies
have applied it in the field of medical imaging. For its long-term dependency,
the transformer is expected to contribute to unconventional convolution neural
net conquer their inherent spatial induction bias. The lately suggested
transformer-based partition method only uses the transformer as an auxiliary
module to help encode the global context into a convolutional representation.
There is hardly any study about how to optimum bond self-attention (the kernel
of transformers) with convolution. To solve the problem, the article proposes
MS-Twins (Multi-Scale Twins), which is a powerful segmentation model on account
of the bond of self-attention and convolution. MS-Twins can better capture
semantic and fine-grained information by combining different scales and
cascading features. Compared with the existing network structure, MS-Twins has
made significant progress on the previous method based on the transformer of
two in common use data sets, Synapse and ACDC. In particular, the performance
of MS-Twins on Synapse is 8% higher than SwinUNet. Even compared with nnUNet,
the best entirely convoluted medical image segmentation network, the
performance of MS-Twins on Synapse and ACDC still has a bit advantage.