When Swin Transformer Meets KANs: An Improved Transformer Architecture for Medical Image Segmentation

Authors

• Nishchal Sapkota, College of Engineering
• Haoyan Shi, College of Engineering
• Yejia Zhang, The University of Texas Rio Grande Valley
• Xianshi Ma, College of Engineering
• Bofang Zheng
• Fabian Vazquez Jr., The University of Texas Rio Grande Valley

Document Type

Conference Proceeding

Publication Date

1-1-2026

Abstract

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 rationalfunction 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 datahungry 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.

Comments

Copyright IEEE.

Recommended Citation

Sapkota, Nishchal, Haoyan Shi, Yejia Zhang, Xianshi Ma, Bofang Zheng, Fabian Vazquez, Pengfei Gu, and Danny Z. Chen. "When Swin Transformer Meets KANs: An Improved Transformer Architecture for Medical Image Segmentation." In 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2026. https://doi.org/10.1109/ISBI61048.2026.11515647

Publication Title

Proceedings International Symposium on Biomedical Imaging

DOI

https://doi.org/10.1109/ISBI61048.2026.11515647