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

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-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-tunes in the same manner. After the dual-model fine-tuning-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 degrees o, , 97. 92 degrees o, , 98. 52 degrees o, , and 98. 85 degrees o, , 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.