MSCF-Net: A Vision Mamba Network with Multi-Scale Context Bridging and Cross-Layer Adaptive Fusion for Medical Image Segmentation

Accurate medical image segmentation remains challenging when lesions have large-scale variation, weak boundaries, and strong background interference. Vision Mamba provides efficient long-range modeling, but current Mamba-based U-shaped networks are still limited by weak local multi-scale representation and coarse skip fusion. This study proposes MSCF-Net, a Vision Mamba segmentation network for dermoscopic and endoscopic images. The network is built on VM-UNet and introduces two modules. The Multi-Scale Context Bridging (MSCB) module enriches bottleneck features with local, dilated, and global context. The Cross-Layer Adaptive Fusion (CLAF) module recalibrates encoder–decoder features in channel and spatial dimensions, reducing noisy shallow feature transmission. A structure loss is used to improve region completeness and boundary quality. Experiments on ISIC 2017, ISIC 2018, and CVC-ClinicDB show Dice scores of 90.62%, 90.82%, and 91.72%, and mIoU values of 82.02%, 82.31%, and 84.56%, respectively. Compared with representative baselines evaluated in our experiments, MSCF-Net achieves competitive segmentation performance under the adopted benchmark protocol. Ablation, qualitative, and spatial response analyses further indicate that MSCB improves scale-aware representation, while CLAF helps the decoder focus on lesion-related cues. The results suggest that MSCF-Net provides a favorable accuracy–efficiency trade-off for medical image segmentation.