Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2408.00273 (eess)
[Submitted on 1 Aug 2024]
Title:3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation
View a PDF of the paper titled 3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation, by Tianze Tang and 2 other authors
View PDFHTML (experimental)Abstract: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.
| Subjects: | Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2408.00273 [eess.IV] |
| (orarXiv:2408.00273v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2408.00273 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled 3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation, by Tianze Tang and 2 other authors
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