MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation

• URL: http://arxiv.org/abs/2505.11797v2
• Date: Fri, 10 Oct 2025 23:38:51 GMT
• Title: MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation
• Authors: Hancan Zhu, Jinhao Chen, Guanghua He,
• Abstract summary: Medical image segmentation has traditionally relied on convolutional neural networks (CNNs) and Transformer-based models.<n>We propose the VSS-Enhanced KAN (VKAN) module, which integrates VSS with the Expanded Field Convolutional KAN (EFC-KAN) as a replacement for Transformer modules.<n>We further embed VKAN into a U-Net frame-work, resulting in MedVKAN, an efficient medical image segmentation model.
• Score: 1.376408511310322
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical image segmentation has traditionally relied on convolutional neural networks (CNNs) and Transformer-based models. CNNs, however, are constrained by limited receptive fields, while Transformers face scalability challenges due to quadratic computational complexity. To over-come these issues, recent studies have explored alternative architectures. The Mamba model, a selective state-space design, achieves near-linear complexity and effectively captures long-range dependencies. Its vision-oriented variant, the Visual State Space (VSS) model, extends these strengths to image feature learning. In parallel, the Kolmogorov-Arnold Network (KAN) enhanc-es nonlinear expressiveness by replacing fixed activation functions with learnable ones. Moti-vated by these advances, we propose the VSS-Enhanced KAN (VKAN) module, which integrates VSS with the Expanded Field Convolutional KAN (EFC-KAN) as a replacement for Transformer modules, thereby strengthening feature extraction. We further embed VKAN into a U-Net frame-work, resulting in MedVKAN, an efficient medical image segmentation model. Extensive exper-iments on five public datasets demonstrate that MedVKAN achieves state-of-the-art performance on four datasets and ranks second on the remaining one. These results underscore the effective-ness of combining Mamba and KAN while introducing a novel and computationally efficient feature extraction framework. The source code is available at: https://github.com/beginner-cjh/MedVKAN.

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