Jan 28, 2024 · GSUNet can achieve segmentation performance comparable to mainstream brain tumor segmentation methods with extremely low model complexity.
GSUNet: A Brain Tumor Segmentation Method Based on 3D Ghost Shuffle U-Net. Authors: JiXuan Hong, JingJing Xie, XueQin He, ChenHui YangAuthors Info & Claims.
GSUNet: A Brain Tumor Segmentation Method Based on 3D Ghost Shuffle U-Net. https://doi.org/10.1007/978-3-031-53305-1_9 ·. Journal: MultiMedia Modeling Lecture ...
Experimental results show that GSUNet can achieve segmentation performance comparable to mainstream brain tumor segmentation methods with extremely low model ...
Article "GSUNet: A Brain Tumor Segmentation Method Based on 3D Ghost Shuffle U-Net" Detailed information of the J-GLOBAL is an information service managed ...
GSUNet: A Brain Tumor Segmentation Method Based on 3D Ghost Shuffle U-Net. MMM (1) 2024: 109-120. [+][–]. Coauthor network. maximize. Note that this feature is ...
We propose a deep learning based approach for automatic brain tumor segmentation utilizing a three-dimensional U-Net extended by residual connections.
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We propose a 3D lightweight GMetaNet for brain tumor segmentation, which adopts local–global modeling strategy. · We propose an enhanced MetaFormer, in which the ...
Missing: GSUNet: Shuffle
In view of the above problems, a brain tumor segmentation method based on 3D Ghost Shuffle U-Net(GSUNet) is proposed. In this paper, the 3D Ghost Module(3D ...
In view of the above problems, a brain tumor segmentation method based on 3D Ghost Shuffle U-Net(GSUNet) is proposed. In this paper, the 3D Ghost Module(3D ...