  Convolutional neural networks (CNNs) have been the consensus for medical
image segmentation tasks. However, they suffer from the limitation in modeling
long-range dependencies and spatial correlations due to the nature of
convolution operation. Although transformers were first developed to address
this issue, they fail to capture low-level features. In contrast, it is
demonstrated that both local and global features are crucial for dense
prediction, such as segmenting in challenging contexts. In this paper, we
propose HiFormer, a novel method that efficiently bridges a CNN and a
transformer for medical image segmentation. Specifically, we design two
multi-scale feature representations using the seminal Swin Transformer module
and a CNN-based encoder. To secure a fine fusion of global and local features
obtained from the two aforementioned representations, we propose a Double-Level
Fusion (DLF) module in the skip connection of the encoder-decoder structure.
Extensive experiments on various medical image segmentation datasets
demonstrate the effectiveness of HiFormer over other CNN-based,
transformer-based, and hybrid methods in terms of computational complexity, and
quantitative and qualitative results. Our code is publicly available at:
https://github.com/amirhossein-kz/HiFormer
