Recent advancements in deep learning for image classification predominantly rely on convolutional neural networks (CNNs) or Transformer-based architectures. However, these models face notable challenges in medical imaging, particularly in capturing intricate texture details and contextual features. Kolmogorov-Arnold Networks (KANs) represent a novel class of architectures that enhance nonlinear transformation modeling, offering improved representation of complex features. In this work, we present MedKAN, a medical image classification framework built upon KAN and its convolutional extensions. MedKAN features two core modules: the Local Information KAN (LIK) module for fine-grained feature extraction and the Global Information KAN (GIK) module for global context integration. By combining these modules, MedKAN achieves robust feature modeling and fusion. To address diverse computational needs, we introduce three scalable variants--MedKAN-S, MedKAN-B, and MedKAN-L. Experimental results on nine public medical imaging datasets demonstrate that MedKAN achieves superior performance compared to CNN- and Transformer-based models, highlighting its effectiveness and generalizability in medical image analysis.

翻译：近年来，图像分类领域的深度学习进展主要依赖于卷积神经网络（CNNs）或基于Transformer的架构。然而，这些模型在医学成像领域面临显著挑战，尤其是在捕捉复杂的纹理细节和上下文特征方面。Kolmogorov-Arnold网络（KANs）代表了一类新颖的架构，它增强了非线性变换建模能力，提供了对复杂特征更优的表示。在本工作中，我们提出了MedKAN，一个基于KAN及其卷积扩展构建的医学图像分类框架。MedKAN包含两个核心模块：用于细粒度特征提取的局部信息KAN（LIK）模块，以及用于全局上下文整合的全局信息KAN（GIK）模块。通过结合这些模块，MedKAN实现了鲁棒的特征建模与融合。为了满足不同的计算需求，我们引入了三个可扩展的变体——MedKAN-S、MedKAN-B和MedKAN-L。在九个公共医学成像数据集上的实验结果表明，与基于CNN和Transformer的模型相比，MedKAN取得了更优的性能，凸显了其在医学图像分析中的有效性和泛化能力。