The U-Net model has consistently demonstrated strong performance in the field of medical image segmentation, with various improvements and enhancements made since its introduction. This paper presents a novel architecture that integrates KAN networks with U-Net, leveraging the powerful nonlinear representation capabilities of KAN networks alongside the established strengths of U-Net. We introduce a KAN-convolution dual-channel structure that enables the model to more effectively capture both local and global features. We explore effective methods for fusing features extracted by KAN with those obtained through convolutional layers, utilizing an auxiliary network to facilitate this integration process. Experiments conducted across multiple datasets show that our model performs well in terms of accuracy, indicating that the KAN-convolution dual-channel approach has significant potential in medical image segmentation tasks.

翻译：U-Net模型在医学图像分割领域一直表现出优异的性能，自提出以来已出现多种改进与增强方法。本文提出了一种将KAN网络与U-Net相结合的新型架构，充分利用KAN网络强大的非线性表示能力以及U-Net的成熟优势。我们引入了一种KAN-卷积双通道结构，使模型能够更有效地捕获局部与全局特征。我们探索了将KAN提取的特征与卷积层获取的特征进行融合的有效方法，并借助辅助网络促进这一整合过程。在多个数据集上进行的实验表明，我们的模型在精度方面表现良好，证明KAN-卷积双通道方法在医学图像分割任务中具有显著潜力。