Consistency learning with dynamic weighting and class-agnostic regularization for semi-supervised medical image segmentation

Author:

Su JiaweiORCID,Luo ZhimingORCID,Lian ShengORCID,Lin Dazhen,Li Shaozi

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference46 articles.

1. Transformation-consistent self-ensembling model for semisupervised medical image segmentation;Li;IEEE Trans. Neural Netw. Learn. Syst.,2020

2. Semi-supervised npc segmentation with uncertainty and attention guided consistency;Hu;Knowl.-Based Syst.,2022

3. Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency;Luo,2021

4. D.-H. Lee, et al., Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, in: Workshop on Challenges in Representation Learning, ICML, Vol. 3, No. 2, 2013, p. 896.

5. In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning;Rizve,2021

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