Medical image segmentation plays a vital role in diagnosis and treatment planning, but remains challenging due to the inherent complexity and variability of medical images, especially in capturing non-linear relationships within the data. We propose U-KABS, a novel hybrid framework that integrates the expressive power of Kolmogorov-Arnold Networks (KANs) with a U-shaped encoder-decoder architecture to enhance segmentation performance. The U-KABS model combines the convolutional and squeeze-and-excitation stage, which enhances channel-wise feature representations, and the KAN Bernstein Spline (KABS) stage, which employs learnable activation functions based on Bernstein polynomials and B-splines. This hybrid design leverages the global smoothness of Bernstein polynomials and the local adaptability of B-splines, enabling the model to effectively capture both broad contextual trends and fine-grained patterns critical for delineating complex structures in medical images. Skip connections between encoder and decoder layers support effective multi-scale feature fusion and preserve spatial details. Evaluated across diverse medical imaging benchmark datasets, U-KABS demonstrates superior performance compared to strong baselines, particularly in segmenting complex anatomical structures.

翻译：医学图像分割在诊断与治疗规划中起着至关重要的作用，但由于医学图像固有的复杂性和变异性，尤其是在捕捉数据中的非线性关系方面，该任务仍然具有挑战性。我们提出了U-KABS，一种新颖的混合框架，它将Kolmogorov-Arnold网络（KANs）的表达能力与U形编码器-解码器架构相结合，以提升分割性能。U-KABS模型结合了卷积与挤压激励阶段（用于增强通道特征表示）以及KAN Bernstein样条（KABS）阶段（该阶段采用基于Bernstein多项式和B样条的可学习激活函数）。这种混合设计利用了Bernstein多项式的全局平滑性和B样条的局部适应性，使模型能够有效捕捉广泛的上下文趋势以及对描绘医学图像中复杂结构至关重要的细粒度模式。编码器与解码器层之间的跳跃连接支持有效的多尺度特征融合并保留空间细节。在多种医学影像基准数据集上的评估表明，与强大的基线模型相比，U-KABS展现出卓越的性能，尤其是在分割复杂解剖结构方面。