# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 1 Introduction  
Accurate image segmentation can more clearly identify changes in anatomic or pathologic structure in medical images [1], which is crucial in various computer-aided diagnostic applications, including lesion contour, surgical planning, and three-dimensional reconstruction. Medical image segmentation can detect and locate the boundaries of lesions in an image, thus helping to quickly identify the potential existence of tumors and cancerous regions, which may help clinicians save diagnosis time and improve the possibility of finding tumors [2]. Traditionally, the symmetric encoder-decoder structures has been the standard in medical image segmentation, where U-Net [3] has become the benchmark of choice among different variants with great success. The U-Net model consists of convolutions, the fundamental operation of which is the convolution operator with two characteristics, weight sharing and local connection, which ensures the affine invariance of the model. Although these characteristics help to create effective and versatile medical imaging systems, they still require additional improvements to aid clinicians in early disease diagnosis [4]. Researchers have proposed various improved methods to add global context to convolutional neural networks (CNNs), among which influential methods include introducing an attention mechanism [5; 6; 7; 8] and expanding convolution kernels [9; 10; 11] to extend their receptive fields. However, the locality of the convolutional layer's receptive fields leads to the inability of networks to utilize remote semantic dependence effectively, so their learning ability is limited to a relatively small area and they fail to fully explore object-level information, especially for organs in termsof texture, shape, and size, typically yielding weaker properties and exhibiting sizable inter-patient variability. Transformer [12] has shown high performance in language learning tasks, and the attention-based model has emerged as an appealing solution because of its ability to efficiently handle very long sequence dependencies, adapting to various vision tasks. Recent research has demonstrated the ability of Transformer modules to entirely take over the role of conventional convolutions by manipulating sequences of picture patches; the most representative of these is Vision Transformer (ViT) [13]. There have been many works proving that the ViT model can promote the development of many computer vision tasks, including semantic segmentation [14], object detection [15], and image classification [13], among others. The accomplishments of ViT in processing natural images have captivated the medical field. In response, researchers are delving into the capabilities of Transformer for medical image segmentation to address the intrinsic receptive field limitations of CNNs and make them suitable for medical imaging applications [16; 17; 18; 19]. However, the performance of Transformer-based models largely depends on pre-training [20; 21]. There are two problems with Transformer-based models' pre-training process. First, there is a scarcity of comprehensive and widely accepted large datasets for pre-training in the medical imaging field due to the extensive time professionals need to dedicate to annotating medical images (in contrast, ImageNet [22], a large dataset, is available for pre-training natural scene images). Secondly, pre-training consumes much time and many computing resources. Moreover, using large natural image datasets for pre-training medical image segmentation models is difficult because of the domain difference between medical and natural pictures. In addition, there are also some open challenges in different types of medical images. For example, when Swin UNETR [23] is pre-trained on a CT dataset and subsequently applied to different medical imaging modalities like MRI, its performance tends to decline. This is attributed to the significant differences in regional characteristics between CT and MRI images [24].