Chinese Journal of Liquid Crystals and Displays, Volume. 39, Issue 8, 1014(2024)

Semi-supervised tongue image segmentation method for traditional chinese medicine based on mutual learning with dual models

Fangxu LI1, Wangming XU1, Xue XU2, and Yun JIA3、*
Author Affiliations
  • 1School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China
  • 2School of Medicine,Wuhan University of Science and Technology,Wuhan 430065,China
  • 3Affiliated Hospital,China University of Geosciences(Wuhan),Wuhan 430074,China
  • show less

    Accurate tongue image segmentation is a crucial prerequisite for objective analysis in tongue diagnosis in traditional Chinese medicine (TCM). At present, the widely-used full-supervised segmentation methods require a large number of pixel-level annotated samples for training, and the single-model-based semi-supervised segmentation methods lack the ability to self-correct the learned error knowledge. To address this issue, a novel semi-supervised tongue image segmentation method based on mutual learning with dual models is proposed. Firstly, model A and B undergo supervised training on the labeled datasets. Subsequently, model A and B enter the mutual learning phase, utilizing a designed mutual learning loss function, in which different weights are assigned based on the disagreement between predictions of the two models on the unlabeled data. Model A generates the pseudo-labels for the unlabeled dataset, and model B fine-tunes on both the labeled dataset and the pseudo-labeled dataset. Then, model B generates the pseudo-labels for the unlabeled dataset, and model A fine-tunes in the same manner. After the dual-model fine-tuning process, the model with better performance is selected as the final tongue image segmentation model. Experimental results show that with labeled data proportions of 1/100, 1/50, 1/25, and 1/8, the mean intersection over union (mIoU) achieved by the proposed method is 96.67%, 97.92%, 98.52%, and 98.85%, respectively, outperforming other typical semi-supervised methods compared. The proposed method achieves high precision in tongue image segmentation with only a small number of labeled data, laying a solid foundation for subsequent applications in TCM such as tongue color, tongue shape and other tongue image analysis, which can promote the digitization of TCM diagnosis and treatment.

    Keywords
    Tools

    Get Citation

    Copy Citation Text

    Fangxu LI, Wangming XU, Xue XU, Yun JIA. Semi-supervised tongue image segmentation method for traditional chinese medicine based on mutual learning with dual models[J]. Chinese Journal of Liquid Crystals and Displays, 2024, 39(8): 1014

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Segmentation

    Received: Sep. 22, 2023

    Accepted: --

    Published Online: Sep. 27, 2024

    The Author Email: Yun JIA (jiayun@cug.edu.cn)

    DOI:10.37188/CJLCD.2023-0308

    Topics