ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation-Reference-Cited by-同舟云学术

ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation

Published:2024-06 Issue:6 Volume:43 Page:2254-2265
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Li Zihan¹^ORCID,Zheng Yuan¹^ORCID,Shan Dandan²,Yang Shuzhou³^ORCID,Li Qingde⁴^ORCID,Wang Beizhan¹^ORCID,Zhang Yuanting⁵^ORCID,Hong Qingqi⁶^ORCID,Shen Dinggang⁷^ORCID

Affiliation:

1. School of Informatics, Xiamen University, Xiamen, China

2. Institute of Artificial Intelligence, Xiamen University, Xiamen, China

3. School of Electronic and Computer Engineering, Peking University, Shenzhen, China

4. School of Computer Science, University of Hull, Hull, U.K

5. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

6. Department of Digital Media Technology and the Xiamen Key Laboratory of Intelligent Storage and Computing, School of Informatics, Xiamen University, Xiamen, China

7. School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Key Research and Development Program of Guangdong Province, China

Open Project Program of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10547147/10423891.pdf?arnumber=10423891

Reference63 articles.

1. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

2. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

3. Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders

4. Learning to Segment From Scribbles Using Multi-Scale Adversarial Attention Gates

5. ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation;Zhang;arXiv:2005.00328,2020

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