# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 1 Introduction
Fully exploiting CNNs' and Transformer's respective advantages to effectively integrate fine-grained and coarse-grained information in images, thereby boosting the precision and performance in deep learning models, has become a research direction researchers are actively working on. In Figure 1, we summarize various medical image segmentation methods that utilize a combined CNN and Transformer hybrid architecture. As shown in Figure 1a, researchers have incorporated Transformer into models with CNN as the backbone in different ways, either by adding them or replacing certain architectural components to create a network that combines Transformer and a CNN in a serial or embedded fashion. However, this strategy only uses stacking to fuse fine-grained and coarse-grained features, which may reduce the fusion effect and not fully leverage the synergistic capabilities of both network types. Figure 1b,c illustrate parallel frameworks of a CNN and Transformer, extracting distinct feature information from both structures and merging them multiple times before passing them to the decoder for decoding. In Figure 1b, additional branches are used to fuse the CNN and Transformer branches, but this introduces network overhead and lacks effective interaction. For example, TransFuse [25] uses a branch composed of BiFusion modules to simultaneously utilize the different characteristics of the CNN branch and the Transformer branch during the feature extraction process, alleviating the problem of ignoring intermediate features due to serial connections. However, its upsampling method struggles to effectively restore the mid-layer information, leading to a loss of detail. In Figure 1c, each layer extracts features using the CNN and Transformer, respectively, and finally, adds and fuses them as the output. However, due to semantic differences between Transformer's and the CNN's features, this strategy limits the fusion's effectiveness. In Figure 1c, the input features are independently extracted by both the Transformer and the CNN, then added and merged as the input of the CNN or Transformer. However, due to the feature semantic differences between Transformer and CNNs, this strategy limits the fusion effect. For example, both HiFormer [26] and CiT-Net [27] use CNN and Transformer branches to extract features, respectively, but HiFormer only implements one-way feature fusion from the CNN to Transformer, while CiT-Net only fuses the two features through a single fusion method and sends them to different branches. Both ignore the different characteristics of the two branches. In our study, we present the collaborative cross-fusion network (CCFNet), designed for medical image segmentation. CCFNet's encoder consists of two parts: first, in the shallow layers of the encoder, a CNN is employed to acquire convolutional depth features, compensating for detail lost in upsampling; second, in the deep encoder layers, a collaborative cross-fusion of Transformer and the CNN is applied, which can simultaneously enhance the image's local and global depictions. As shown in Figure 1d, compared with other combination strategies, CCFNet achieves close information interaction between the CNN and Transformer while continuously modeling local and global representations. By continuously aggregating the hierarchical representation and information interaction of global and local features, the information interaction of collaborative cross-fusion is closer and the feature fusion is more thorough. This makes CCFNet excellent in medical image segmentation tasks. In CCFNet, high-resolution features extract fine-grained local information and perform depthwise convolutions. Since low-resolution features contain more global information (location and semantic information), feature prediction can fuse long-distance global information, and the self-attention mechanism facilitates the capture of deep information [28]. CCFNet processes low-resolution features through a parallel fusion of the CNN and Transformer within the collaborative cross-fusion module (CCFM).