Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image Segmentation Under Limited Computational Resources-Reference-Cited by-同舟云学术

Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image Segmentation Under Limited Computational Resources

Published:2023 Issue: Volume: Page:1-1
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Pang Yan¹^ORCID,Liang Jiaming¹^ORCID,Huang Teng¹^ORCID,Chen Hao²^ORCID,Li Yunhao¹^ORCID,Li Dan¹^ORCID,Huang Lin³,Wang Qiong⁴^ORCID

Affiliation:

1. Institute of Artificial Intelligence and Blockchain, Guangzhou University, China

2. Department of Computer Science and Engineering and the Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, China

3. Department of Engineering and Engineering Technology College of Aerospace, Computing, Engineering, and Design (ACED), Metropolitan State University of Denver, USA

4. Shenzhen Institute of Advanced Technology, Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Chinese Academy of Sciences, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/4359023/10288609.pdf?arnumber=10288609

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