# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 2 Related Work  
### Cnn  
The first widely known CNN model was U-Net [3], named because of its U-shaped network structure, which consisted of an encoder-decoder symmetrical network composed of convolution, downsampling, upsampling, and stitching operations. U-Net employed an encoder to capture global contextual information by downsampling feature representations. Conversely, the decoder restored these features to the original input resolution for semantic prediction through upsampling. Additionally, skip connections linked outputs from the encoder with corresponding decoder layers at the same resolution, helping to recover spatial details lost in downsampling and enhancing detail preservation. V-Net [29] was developed specifically for 3D image segmentation, focusing on direct, end-to-end training to facilitate volume segmentation in MRI scans of the prostate.  
Most U-Net variants were based on the residual block [30], dense block [31], and attention mechanism [5; 6; 7] to improve the network's segmentation capabilities. The concepts behind dense and residual blocks can increase the rate of feature reuse and alleviate the problem of vanishing gradients. Dense-UNet [32] and Res-UNet [33], inspired by dense and residual connections, respectively, adapted the U-Net structure by replacing its sub-modules with versions that incorporate these connections, enhancing the network's architecture. UNet++ [34] added the dense block and convolutional layers between its decoder and encoder, aimed at narrowing the semantic differences in its processed features to improve segmentation accuracy. MultiResUNet [35] innovatively combined the MultiRes module and U-Net, in which the MultiRes module extends the concept of residual connections. In this configuration, feature maps produced by three \(3\times 3\) convolutions were spliced together as a combined feature map and then added to the outcome of a \(1\times 1\) convolution performed on the input feature map.  
An attention mechanism in the network enables it to concentrate on vital sections while suppressing irrelevant input to improve processing efficiency. AttnUNet [8] introduced an attention mechanism into U-Net, adjusting encoder output features before they are concatenated with matching decoder features at each resolution level. This attention setup generates a gating signal that modulates feature importance at different spatial locations. FocusNet [36] employed the dual encoder-decoder architecture, leveraging attention gating to transfer relevant features from the decoder of one U-Net to the encoder of another. This mechanism enhances the propagation of features throughout the network.  
On the improvement of a non-network structure, the authors of nnUNet [37] asserted that further enhancements could be achieved by gaining deeper insights into the data and training techniques tailored to medical data and implementing suitable preprocessing, and proposed a robust U-Net-based adaptive framework (including 2D and 3D frameworks) that automatically adjusted itself to preprocess data and selected the best network architecture for the task without human intervention.