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

本文提出了一种新的方法HiFormer，它通过使用Swin Transformer模块和基于CNN的编码器设计了两种多尺度特征表示来有效地跨越CNN和Transformer进行医学图像分割。在编码器解码器结构的跳跃连接中，我们提出了Double-Level Fusion（DLF）模块，以确保从两个上述表示中获得的全局和局部特征的细粒度融合。在各种医学图像分割数据集上的广泛实验表明，HiFormer在计算复杂度、定量和定性结果方面优于其他基于CNN、Transformer和混合方法。

HiFormer: 使用Transformer实现的分层多尺度表示，用于医学图像分割