Abstract
Due to the scalability issues of transformers and the limitations of CNN's lack of typical inductive bias, their applications in a wider range of fields are somewhat restricted. Therefore, the hybrid network architecture that combines the advantages of convolution and Transformer is gradually becoming a hot research and application direction. This article proposes an enhanced dual encoder network (EDE-Net) that integrates convolution and pyramid transformers for medical image segmentation. Specifically, we apply convolutional kernels and pyramid transformer structures in parallel in the encoder to extract features, ensuring that the network can capture local details and global semantic information. To efficiently fuse local details information and global features at each downsampling stage, we introduce the phase-based iterative feature fusion module (PIFF). The PIFF module first combines local details and global features and then assigns distinct weight coefficients to each, distinguishing their importance for foreground pixel classification. By effectively balancing the significance of local details and global features, the PIFF module enhances the network's ability to delineate fine lesion edges. Experimental results on the GlaS and MoNuSeg datasets validate the effectiveness of this approach. On these two publicly available datasets, our EDE-Net significantly outperforms previous CNN-based (such as UNet) and transformer-based (such as Swin-UNet) algorithms.