# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 2 Related Work
### Transformer  
The groundbreaking Transformer architecture [12] sparked a paradigm shift in natural language processing, swiftly establishing itself as a commonly used foundational model for visual comprehension tasks. Transformer has great visuals because of its ability to create distinct visual environments, but it also has an inherent drawback of not being able to exploit spatial environments in images as a CNN does. Recent works have evolved toward possible solutions to overcome this drawback, and most extant studies in medical image segmentation employ CNN-Transformer hybrid models for feature processing. TransUNet [16] introduced the integration of the Transformer architecture into medical image segmentation frameworks. It introduced a novel approach by framing the segmentation task as a sequence-to-sequence prediction, incorporating self-attention mechanisms. This model adopts a hybrid CNN-Transformer design, strategically combining the spatial details extracted by CNN features with the Transformer's capabilities. Additionally, within the realm of medical image segmentation, several other hybrid models, built upon the U-Net architecture, have also seen enhancements. For example, UCTransNet [17] adopted the Channel Transformer (CTrans) module, leveraging the channel attention mechanism as a modification to the traditional U-Net skip connections. This implementation includes one sub-module designed for channel-wise cross-attention via Transformer technology and another for multi-scale channel fusion. These enhancements facilitate the efficient integration of multi-scale channel data with decoder features to enhance clarity and resolution. Like TransUNet, UNETR [18] employed a Transformer along with a convolutional decoder in its encoder architecture to build segmentation maps. MT-UNet [19] developed a U-shaped network, utilizing the mixed Transformer module (MTM) designed for both intra-sample and inter-sample learning, aimed at enhancing the precision of medical image segmentation. First, the MTM applied the LGG-SA module to assess self-similarity. It then explored inter-sample connections using an external attention mechanism. MSFusion-UNet [38] adopted a flexible and efficient multi-stream fusion encoder to facilitate multi-scale fusion of multiple imaging stream features through spatial attention. LeVit-UNet [39] used LeViT blocks to build a U-Net variant of the encoder, which enables it to learn long-range dependencies more efficiently. DCA module [40] used double cross-attention on the U-Net framework and enhanced skip connections to solve the semantic gap between encoder and decoder features. TransAttUnet [41] adopted an effective feature screening method by jointly designing multi-scale skip connections and multi-level guided attention. It learned non-local interactions between encoding features and passed key features to the decoder. From a macro perspective, these architectures integrated a CNN and Transformer in a serial combination manner. In terms of parallel strategies, TransFuse [25] implemented the BiFusion module, which performed spatial attention on the CNN branch and channel attention on the Transformer branch. Following this, operations such as convolution, multiplication, concatenation, and residuals were executed to facilitate the merging of features from both branches. On this basis, FAFuse [42] improved the BiFusion module through a four-axis fusion module to improve the ability of representation learning. The HiFormer [26] algorithm uses CNN and Transformer branches to extract features. To provide positioning information and reuse features, it incorporates a skip connection that conveys local CNN features to the Transformer. CiT-Net [27] employs dynamically deformable convolutions within its CNN branch to enhance feature extraction capabilities, while it incorporates the compact convolution projection and the SW-ACAM module in the Transformer branch to more effectively capture long-term dependencies across dimensions.