查看文章

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they inevitably 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 Convolutional neural network 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 outline. 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, quantitative and qualitative results