  Convolutional neural network (CNN) based methods have achieved great
successes in medical image segmentation, but their capability to learn global
representations is still limited due to using small effective receptive fields
of convolution operations. Transformer based methods are capable of modelling
long-range dependencies of information for capturing global representations,
yet their ability to model local context is lacking. Integrating CNN and
Transformer to learn both local and global representations while exploring
multi-scale features is instrumental in further improving medical image
segmentation. In this paper, we propose a hierarchical CNN and Transformer
hybrid architecture, called ConvFormer, for medical image segmentation.
ConvFormer is based on several simple yet effective designs. (1) A feed forward
module of Deformable Transformer (DeTrans) is re-designed to introduce local
information, called Enhanced DeTrans. (2) A residual-shaped hybrid stem based
on a combination of convolutions and Enhanced DeTrans is developed to capture
both local and global representations to enhance representation ability. (3)
Our encoder utilizes the residual-shaped hybrid stem in a hierarchical manner
to generate feature maps in different scales, and an additional Enhanced
DeTrans encoder with residual connections is built to exploit multi-scale
features with feature maps of different scales as input. Experiments on several
datasets show that our ConvFormer, trained from scratch, outperforms various
CNN- or Transformer-based architectures, achieving state-of-the-art
performance.
