文献类型：期刊文献

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

作者：Zhu, Hancan[1]; Chen, Jinhao[1]; He, Guanghua[1]

机构：[1] School of Mathematics, Physics and Information, Shaoxing University, Zhejiang, Shaoxing, 312000, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250216044)

语种：英文

外文关键词：Convolutional neural networks - Fuzzy logic - Image enhancement - NP-hard - Photointerpretation - State space methods

外文摘要：Medical image segmentation relies heavily on convolutional neural networks (CNNs) and Transformer-based models. However, CNNs are constrained by limited receptive fields, while Transformers suffer from scalability challenges due to their quadratic computational complexity. To address these limitations, recent advances have explored alternative architectures. The state-space model Mamba offers near-linear complexity while capturing long-range dependencies, and the Kolmogorov-Arnold Network (KAN) enhances nonlinear expressiveness by replacing fixed activation functions with learnable ones. Building on these strengths, we propose MedVKAN, an efficient feature extraction model integrating Mamba and KAN. Specifically, we introduce the EFC-KAN module, which enhances KAN with convolutional operations to improve local pixel interaction. We further design the VKAN module, integrating Mamba with EFC-KAN as a replacement for Transformer modules, significantly improving feature extraction. Extensive experiments on five public medical image segmentation datasets show that MedVKAN achieves state-of-the-art performance on four datasets and ranks second on the remaining one. These results validate the potential of Mamba and KAN for medical image segmentation while introducing an innovative and computationally efficient feature extraction framework. The code is available at: https://github.com/beginner-cjh/MedVKAN. ? 2025, CC BY-NC-SA.