用新型神经网络提升医学图像分类准确率
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification
- 基于柯尔莫哥洛夫-阿诺德网络设计双模块结构
- 在9个数据集上超越传统模型表现
- 适合需要高精度医学影像分析的场景
深度学习在图像分类中主要依赖卷积神经网络（CNN）或基于Transformer的架构，但在医学影像中难以捕捉复杂的纹理细节和上下文特征。柯尔莫哥洛夫-阿诺德网络（KANs）是一类新型架构，能更好建模非线性变换，提升复杂特征表达能力。本文提出MedKAN，一种基于KAN及其卷积扩展的医学图像分类框架。其核心包含两个模块：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融合的全局信息KAN（GIK）模块。通过两者的结合，实现鲁棒的特征建模与融合。为满足不同计算需求，设计了三种可扩展变体：MedKAN-S、MedKAN-B和MedKAN-L。在九个公开医学影像数据集上的实验表明，MedKAN性能优于现有CNN和Transformer模型，验证了其在医学图像分析中的有效性与泛化能力。
原文摘要 · Abstract (English)
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.
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