Generative adversarial DacFormer network for MRI brain tumor segmentation
Abstract Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pur...
Saved in:
| Main Authors: | Muqing Zhang, Qiule Sun, Yutong Han, Mingli Zhang, Wei Wang, Jianxin Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02714-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans
by: Yu Lyu, et al.
Published: (2025-01-01) -
A Robust U-Net-Based Approach for Accurate Brain Tumor Segmentation Using Multimodal MRI Data
by: Mohammad Talal Ghazal
Published: (2023-11-01) -
Application of Generative Adversarial Networks Based on Global and Local Feature Information in Hippocampus Segmentation
by: WEI Zhihong, et al.
Published: (2025-06-01) -
Graph convolution-based adaptive feature fusion method for MRI brain tumor segmentation
by: Ye ZHANG, et al.
Published: (2025-08-01) -
Image Generation and Lesion Segmentation of Brain Tumors and Stroke Based on GAN and 3D ResU-Net
by: Mingkang Sun, et al.
Published: (2025-01-01)