Multi-scale Neighborhood Attention Transformer on U-Net for Medical Image Segmentation-Reference-Cited by-同舟云学术

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Multi-scale Neighborhood Attention Transformer on U-Net for Medical Image Segmentation

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Zhang Nanxing¹,Ma Shiqiang²,Li Xuejian¹,Zhang Jiahui³,Tang Jijun⁴,Guo Fei⁵

Affiliation:

1. Tianjin University,College of Intelligence and Computing,Tianjin,China

2. Tianjin University,School of Computer Science and Technology College of Intelligence and Computing,Tianjin,China

3. Central South University,School of Physics and Electronics,Changsha,China

4. Shenzhen Institute of Advanced Technology Chinese Academy of Sciences,Shenzhen,China

5. Central South University,School of Computer Science and Engineering,Changsha,China

Funder

National Natural Science Foundation of China

Young Scientists Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09994872.pdf?arnumber=9994872

Reference21 articles.

1. Deep Residual Learning for Image Recognition

2. Transunet: Transformers make strong encoders for medical image segmentation;chen;arXiv preprint arXiv 2102 05988,2021

3. Attention is all you need;vaswani;Advances in neural information processing systems,2017

Cited by 4 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. Aggregate-aware model with bidirectional edge generation for medical image segmentation;Applied Soft Computing;2024-09

2. Power Line Detection Based on Subpixel-Neighborhood Attention in Complex Terrain Backgrounds;IEEE Sensors Journal;2024-05-01

3. MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

4. SCTransNet: 3D Medical Image Segmentation Model Based on the Fusion of CNN and Transformer;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

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