SUNeXt: Lightweight Medical Image Segmentation Network Based on Grouped Feature Fusion and Shifted Large Kernel Convolution

ABSTRACT To solve efficient image segmentation in practical medical applications in resource‐constrained point‐of‐care environments, the lightweight medical image segmentation network is proposed based on grouped feature fusion and large kernel convolution, which introduces a U‐shaped, convolution‐based architecture that significantly reduces parameters and computational cost. The proposed model combines shifted large kernel convolution with grouped feature fusion technique in a lightweight and attention‐free way, which is specifically designed to fuse features to capture global context. Meanwhile, the grouped multi‐scale feature fusion module is proposed to achieve effective cross‐layer connectivity and efficient fusion of multi‐scale features by grouping deep and shallow features and subsequently applying a lightweight grouped large kernel convolution. The extensive experiments on multiple datasets verify that our model outperforms current popular models in image segmentation with lower parameter quantity and computational cost, and achieves industry‐leading performance with low resource consumption.