用KAN与Mamba改进UNet，提升医学图像分割效率与精度
KM-UNet KAN Mamba UNet for medical image segmentation
- 结合KAN的高效表征与Mamba的长程建模能力
- 在5个数据集上达到顶尖性能，计算开销更低
- 首个融合KAN与Mamba的医学图像分割框架，适合医疗AI研究者
医学图像分割是医学影像分析的关键任务。传统基于CNN的方法难以建模长距离依赖，而基于Transformer的模型虽表现优异，却面临二次方复杂度问题。为此，我们提出KM-UNet——一种新型U型网络架构，融合柯尔莫哥洛夫-阿诺德网络（KAN）与状态空间模型（SSM）。KM-UNet利用柯尔莫哥洛夫-阿诺德表示定理实现高效特征表示，并通过SSM实现可扩展的长程建模，在准确率与计算效率间取得平衡。我们在ISIC17、ISIC18、CVC、BUSI和GLAS五个基准数据集上评估该方法。实验结果表明，KM-UNet在医学图像分割任务中性能媲美最先进方法。据我们所知，这是首个将KAN与SSM集成的医学图像分割框架。本工作为构建更高效、可解释的医学图像分割系统提供了重要基线与新视角。代码已开源：https://github.com/2760613195/KM_UNet。
原文摘要 · Abstract (English)
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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