  We explore the application of U-KAN, a U-Net based network enhanced with
Kolmogorov-Arnold Network (KAN) layers, for 3D brain tumor segmentation using
multi-modal MRI data. We adapt the original 2D U-KAN model to the 3D task, and
introduce a variant called UKAN-SE, which incorporates Squeeze-and-Excitation
modules for global attention. We compare the performance of U-KAN and UKAN-SE
against existing methods such as U-Net, Attention U-Net, and Swin UNETR, using
the BraTS 2024 dataset. Our results show that U-KAN and UKAN-SE, with
approximately 10.6 million parameters, achieve exceptional efficiency,
requiring only about 1/4 of the training time of U-Net and Attention U-Net, and
1/6 that of Swin UNETR, while surpassing these models across most evaluation
metrics. Notably, UKAN-SE slightly outperforms U-KAN.
