3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation
<|reference_start|>3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation: 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.<|reference_end|>
@article{tang20243d,
    title={3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation},
    author={Tianze Tang, Yanbing Chen, Hai Shu},
    journal={arXiv preprint arXiv:2408.00273},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00273},
    primaryClass={eess.IV cs.CV}
}