Medical image segmentation is fundamental for delineating lesion and organ boundaries in clinical workflows. While UNet-based models remain widely used, CNN-dominant designs are limited in modeling long-range context, and Transformer-based variants often introduce substantial computational overhead due to quadratic attention. To address this issue, we propose KMP-UNet, a parallel U-shaped framework that combines a Mamba-based state-space branch for linear-complexity contextual modeling and a Kolmogorov–Arnold Network (KAN) branch for nonlinear feature representation. We further introduce a task-oriented fusion block and a skip refinement module to better exploit hierarchical encoder–decoder features. KMP-UNet has a compact model size (about 1.0M parameters in our implementation). We evaluate the proposed method on four public datasets (ISIC2017, ISIC2018, CVC-ClinicDB, and BUSI) using standard segmentation metrics. On ISIC2018, KMP-UNet achieves 0.9038 DSC and 0.9600 accuracy under our protocol. Extensive comparisons and targeted ablations are conducted to analyze the contribution of each component.
Liu et al. (Sun,) studied this question.