基于双模型互学习的半监督中医舌诊图像分割方法OA北大核心CSTPCD

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.