3D-DDA: 3D Dual-Domain Attention for Brain Tumor Segmentation-Reference-Cited by-同舟云学术

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3D-DDA: 3D Dual-Domain Attention for Brain Tumor Segmentation

Published:2023-10-08 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Image Processing (ICIP)
language:
Short-container-title:

Author:

Do Nhu-Tai¹,Vo-Thanh Hoang-Son²,Nguyen-Quynh Tram-Tran²,Kim Soo-Hyung³

Affiliation:

1. Institute of Intelligent and Interactive Technologies, University of Economics Ho Chi Minh City,UEH,Vietnam

2. HCMC University of Foreign Language-Information Technology,Department of Information Technology,Vietnam

3. Chonnam National University,Department of Artificial Intelligence Convergence,Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10221937/10221892/10222602.pdf?arnumber=10222602

Reference16 articles.

1. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

2. Attention mechanisms in computer vision: A survey

3. 3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation

4. A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation

5. BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MPEDA-Net: A lightweight brain tumor segmentation network using multi-perspective extraction and dense attention;Biomedical Signal Processing and Control;2024-05

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