KM-UNet：用于医学图像分割的 KAN Mamba UNet
KM-UNet KAN Mamba UNet for medical image segmentation
AI总结：
本文提出融合 KAN 与状态空间模型的 U 形网络 KM-UNet，以兼顾长程建模能力和计算效率，并在五个医学分割基准上验证其竞争力。
摘要：
医学图像分割是医学影像分析中的一项关键任务。传统基于 CNN 的方法难以建模长程依赖，而基于 Transformer 的模型尽管取得了成功，却受到二次计算复杂度的困扰。为克服这些局限，我们提出 KM-UNet，这是一种新颖的 U 形网络架构，结合了 Kolmogorov-Arnold Networks（KAN）与状态空间模型（SSM）的优势。KM-UNet 利用 Kolmogorov-Arnold 表示定理实现高效特征表示，并利用 SSM 进行可扩展的长程建模，从而在精度与计算效率之间取得平衡。我们在五个基准数据集上评估 KM-UNet：ISIC17、ISIC18、CVC、BUSI 和 GLAS。实验结果表明，与医学图像分割任务中的最先进方法相比，KM-UNet 取得了具有竞争力的性能。据我们所知，KM-UNet 是首个整合 KAN 与 SSM 的医学图像分割框架。本工作为开发更高效、更具可解释性的医学图像分割系统提供了有价值的基线和新的见解。代码已在 https://github.com/2760613195/KM_UNet 开源。
英文摘要：
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
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