• Optoelectronics Letters
  • Vol. 18, Issue 9, 547 (2022)
Enrong ZHU1, Haochen ZHAO2, and Xiaofei and HU1、*
Author Affiliations
  • 1School of Geography and Bioinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • 2School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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    DOI: 10.1007/s11801-022-2010-0 Cite this Article
    ZHU Enrong, ZHAO Haochen, and HU Xiaofei. Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning[J]. Optoelectronics Letters, 2022, 18(9): 547 Copy Citation Text show less
    References

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    [2] HE K, FAN H, WU Y, et al. Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York:IEEE, 2020:9729-9738.

    [3] CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]//International Conference on Machine Learning, July 12-18, 2020, Vienna, Austria. San Diego:ICML, 2020:1597-1607.

    [4] LEE D H. Pseudo-label : the simple and efficient semi-supervised learning method for deep neural networks[C]//Workshop on Challenges in Representation Learning, June 16-21, 2013, Atlanta, GA. San Diego: ICML, 2013:896.

    [5] KHOSLA P, TETERWAK P, WANG C, et al. Supervised contrastive learning[J]. Advances in neural information processing systems, 2020, 33:18661-18673.

    [6] CHAITANYA K, ERDIL E, KARANI N, et al. Contrastive learning of global and local features for medical image segmentation with limited annotations[J]. Advances in neural information processing systems, 2020, 33:12546-12558.

    [7] ZHENG X, FU C, XIE H, et al. Uncertainty-aware deep co-training for semi-supervised medical image segmentation[EB/OL]. (2021-11-23) [2022-04-26]. https://arxiv.org/abs/2111.11629v1.

    [8] CHAKRABORTY S, GOSTHIPATY A R, PAUL S. G-SimCLR:self-supervised contrastive learning with guided projection via pseudo labelling[C]//2020 International Conference on Data Mining Workshops (ICDMW), November 17-20, 2020, Sorrento, Italy. New York:IEEE, 2020:912-916.

    [9] DIPPEL J, VOGLER S, H?HNE J. Towards fine-grained visual representations by combining contrastive learning with image reconstruction and attention-weighted pooling[EB/OL]. (2021-04-09)[2022-04-26]. https://arxiv.org/abs/2104.04323.

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    [11] ZHAO X, FANG C, FAN D J, et al. Cross-level contrastive learning and consistency constraint for semi-supervised medical image segmentation[EB/OL]. (2021-02-13) [2022-04-26]. https://arxiv.org/abs/2202.04074.

    ZHU Enrong, ZHAO Haochen, and HU Xiaofei. Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning[J]. Optoelectronics Letters, 2022, 18(9): 547
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