Coarse-Refined Consistency Learning Using Pixel-Level Features for Semi-Supervised Medical Image Segmentation-Reference-Cited by-同舟云学术

Coarse-Refined Consistency Learning Using Pixel-Level Features for Semi-Supervised Medical Image Segmentation

Published:2023-08 Issue:8 Volume:27 Page:3970-3981
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Du Jie¹^ORCID,Zhang Xiaoci¹,Liu Peng²^ORCID,Wang Tianfu¹^ORCID

Affiliation:

1. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China

2. Department of Computer and Information Science, University of Macau, Taipa, Macau

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Foundation for Distinguished Young Talents in Higher Education of Guangdong

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10210140/10131969.pdf?arnumber=10131969

Reference46 articles.

1. Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations

2. How distance transform maps boost segmentation CNNS: An empirical study;ma;Proc Med Imag Deep Learn,0

3. Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation

4. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning;gal;Proc Int Conf Mach Learn,0

5. Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering

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