标题： KM-UNet KAN Mamba UNet 用于医学图像分割 显示英文标题

标题： KM-UNet KAN Mamba UNet for medical image segmentation

摘要： 医学图像分割是医学影像分析中的关键任务。传统的基于CNN的方法在建模长距离依赖关系方面存在困难，而基于Transformer的模型尽管取得了成功，但面临着二次计算复杂度的问题。为了解决这些限制，我们提出了KM-UNet，这是一种结合了Kolmogorov-Arnold网络（KANs）和状态空间模型（SSMs）优势的新型U型网络架构。KM-UNet利用Kolmogorov-Arnold表示定理进行高效的特征表示，并利用状态空间模型进行可扩展的长距离建模，实现了准确性和计算效率之间的平衡。我们在五个基准数据集上评估了KM-UNet：ISIC17、ISIC18、CVC、BUSI和GLAS。实验结果表明，与最先进的方法相比，KM-UNet在医学图像分割任务中表现出具有竞争力的性能。据我们所知，KM-UNet是第一个整合KANs和SSMs的医学图像分割框架。这项工作为开发更高效和可解释的医学图像分割系统提供了有价值的基线和新的见解。代码在https://github.com/2760613195/KM_UNet开源。 关键词：KAN，Manba，状态空间模型，UNet，医学图像分割，深度学习 显示英文摘要

摘要： Medical image segmentation is a critical task in medical imaging analysis. Traditional CNN-based methods struggle with modeling long-range dependencies, while Transformer-based models, despite their success, suffer from quadratic computational complexity. To address these limitations, we propose KM-UNet, a novel U-shaped network architecture that combines the strengths of Kolmogorov-Arnold Networks (KANs) and state-space models (SSMs). KM-UNet leverages the Kolmogorov-Arnold representation theorem for efficient feature representation and SSMs for scalable long-range modeling, achieving a balance between accuracy and computational efficiency. We evaluate KM-UNet on five benchmark datasets: ISIC17, ISIC18, CVC, BUSI, and GLAS. Experimental results demonstrate that KM-UNet achieves competitive performance compared to state-of-the-art methods in medical image segmentation tasks. To the best of our knowledge, KM-UNet is the first medical image segmentation framework integrating KANs and SSMs. This work provides a valuable baseline and new insights for the development of more efficient and interpretable medical image segmentation systems. The code is open source at https://github.com/2760613195/KM_UNet Keywords:KAN,Manba, state-space models,UNet, Medical image segmentation, Deep learning