用KAN与U-Net双通道融合，提升医学图像分割精度
KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation
- 设计KAN-卷积双通道结构，协同捕捉局部与全局特征
- 在多个数据集上实现更高分割准确率，验证方法有效性
- 适合关注医学图像分割新架构的科研人员与临床应用开发者
U-Net模型在医学图像分割领域表现优异，自提出以来不断有改进。本文提出一种将KAN网络与U-Net结合的新架构，利用KAN强大的非线性表征能力与U-Net的结构优势。引入KAN-卷积双通道结构，增强模型对局部和全局特征的捕获能力；通过辅助网络实现KAN特征与卷积特征的有效融合。在多个数据集上的实验表明，该模型在分割精度方面表现良好，验证了KAN-卷积双通道方法在医学图像分割任务中的显著潜力。
原文摘要 · Abstract (English)
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.
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