CMUNEXT: An Efficient Medical Image Segmentation Network Based on Large Kernel and Skip Fusion

The u-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a fully convolutional segmentation network with u-shaped architecture struggles to effectively extract global context information, which is vital for the precise localization of lesions. While hybrid architectures combining CNN and Transformer can address these issues, their applications are limited due to the computational resource. In addition, the inductive bias of convolution in lightweight networks adeptly fits the scarce medical data, which is lacking in the Transformer based network. To extract global context information while…

• AO
  Academy of Medical Sciences