A hybrid Kolmogorov-Arnold network for medical image segmentation

TL;DR

The paper introduces U-KABS, a hybrid neural network combining Kolmogorov-Arnold Networks with a U-shaped architecture, significantly improving medical image segmentation by capturing complex patterns and structures.

Contribution

It presents a novel hybrid framework integrating KANs with U-Net architecture, enhancing segmentation accuracy for complex medical images.

Findings

U-KABS outperforms baseline models on multiple medical imaging datasets.

The hybrid model effectively captures both global and local features.

Enhanced segmentation of complex anatomical structures.

Abstract

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.