当Swin Transformer遇见KANs：一种改进的Transformer架构用于医学图像分割
When Swin Transformer Meets KANs: An Improved Transformer Architecture for Medical Image Segmentation
AI总结：
UKAST结合KANs与Swin Transformer，提升医学图像分割的效率与准确性，尤其在数据稀缺情况下表现优异。
摘要：
医学图像分割对于准确的诊断和治疗计划至关重要，但仍然具有挑战性，因为复杂的解剖结构和有限的标注训练数据使其困难。基于CNN的分割方法在局部特征提取方面表现出色，但在建模长距离依赖关系方面存在困难。相比之下，Transformer能够更有效地捕捉全局上下文，但本质上数据需求高且计算成本高。在本工作中，我们引入了UKAST，一种类似U-Net的架构，将基于有理函数的Kolmogorov-Arnold网络（KANs）整合到Swin Transformer编码器中。通过利用有理基函数和Kolmogorov-Arnold Transformer（KAT）中的分组有理KANs（GR-KANs），我们的架构解决了传统样条基KANs的效率问题，从而获得更具表达力和数据效率的框架，同时减少了FLOPs并仅在参数数量上产生极小的增加，相比SwinUNETR。UKAST在四个不同的2D和3D医学图像分割基准测试中实现了最先进的性能，始终优于基于CNN和Transformer的基线。值得注意的是，它在数据稀缺的环境中实现了更高的准确性，缓解了标准视觉Transformer的数据需求限制。这些结果展示了KAN增强的Transformer在推进数据高效医学图像分割方面的潜力。代码可在：https://github.com/nsapkota417/UKAST获取。
英文摘要：
Medical image segmentation is critical for accurate diagnostics and treatment planning, but remains challenging due to complex anatomical structures and limited annotated training data. CNN-based segmentation methods excel at local feature extraction, but struggle with modeling long-range dependencies. Transformers, on the other hand, capture global context more effectively, but are inherently data-hungry and computationally expensive. In this work, we introduce UKAST, a U-Net like architecture that integrates rational-function based Kolmogorov-Arnold Networks (KANs) into Swin Transformer encoders. By leveraging rational base functions and Group Rational KANs (GR-KANs) from the Kolmogorov-Arnold Transformer (KAT), our architecture addresses the inefficiencies of vanilla spline-based KANs, yielding a more expressive and data-efficient framework with reduced FLOPs and only a very small increase in parameter count compared to SwinUNETR. UKAST achieves state-of-the-art performance on four diverse 2D and 3D medical image segmentation benchmarks, consistently surpassing both CNN- and Transformer-based baselines. Notably, it attains superior accuracy in data-scarce settings, alleviating the data-hungry limitations of standard Vision Transformers. These results show the potential of KAN-enhanced Transformers to advance data-efficient medical image segmentation. Code is available at: https://github.com/nsapkota417/UKAST
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
