All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation-Reference-Cited by-同舟云学术

All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation

Published:2022-07 Issue:7 Volume:26 Page:3174-3184
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Xu Zhe¹^ORCID,Wang Yixin²^ORCID,Lu Donghuan³^ORCID,Yu Lequan⁴^ORCID,Yan Jiangpeng⁵^ORCID,Luo Jie⁶,Ma Kai³^ORCID,Zheng Yefeng³^ORCID,Tong Raymond Kai-yu¹^ORCID

Affiliation:

1. Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China

2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

3. Tencent Healthcare (Shenzhen) Company, LTD and Tencent Jarvis Lab, Shenzhen, China

4. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China

5. Department of Automation, Tsinghua University, Beijing, China

6. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Funder

National Key R&D Program of China

General Research Fund from Research Grant Council of Hong Kong

Tencent Healthcare

Tencent Jarvis Lab

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/ielx7/6221020/9813470/09741294.pdf?arnumber=9741294

Reference46 articles.

1. Feature Weighting and Boosting for Few-Shot Segmentation

2. Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

3. SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

4. Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency

5. Semi-supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

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