Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation-Reference-Cited by-同舟云学术

Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

Published:2023-11 Issue:11 Volume:42 Page:3244-3255
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Wu Huimin¹^ORCID,Li Xiaomeng²^ORCID,Lin Yiqun²,Cheng Kwang-Ting³^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China

2. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China

3. Department of Electronic and Computer Engineering and the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China

Funder

National Natural Science Foundation of China/HKSAR Research Grants Council Joint Research Scheme

Shenzhen Municipal Central Government Guides Local Science and Technology Development Special Funded Projects

Beijing Institute of Collaborative Innovation (BICI) under Collaboration with The Hong Kong University of Science and Technology

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/ielx7/42/10299534/10132102.pdf?arnumber=10132102

Reference63 articles.

1. Semi-supervised Medical Image Segmentation through Dual-task Consistency

2. A large annotated medical image dataset for the development and evaluation of segmentation algorithms;simpson;arXiv 1902 09063,2019

3. DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images

4. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

5. Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning

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