Weakly supervised multiple instance active learning for tooth-marked tongue recognition

Feilin DengShangxuan LiZizhu YangWu Zhou^*

• School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China

The recognition of tooth-marked tongue has important clinical diagnostic value in Traditional Chinese Medicine. Current deep learning methods for tooth mark detection require extensive manual labeling and tongue segmentation, which is labor-intensive. Therefore, we propose a weakly supervised multiple instance active learning model for tooth-marked tongue recognition, aiming to eliminate preprocessing segmentation and reduce the annotation workload while maintaining diagnostic accuracy. We propose a one-stage method to generate tooth mark instances, which eliminates the need for pre-segmentation of the tongue. To make full use of unlabeled data, we introduce a semi-supervised learning paradigm to pseudo label unlabeled tongue images with high model confidence in active learning and incorporate them into the training set, so as to improve the training efficiency of active learning model. In addition, We propose an instance-level hybrid query method considering the diversity of tooth marks. Experimental results on clinical tongue images verify the effectiveness of the proposed method, which achieves an accuracy of 93.88% for tooth-marked tongue recognition, outperforming the recently introduced weakly supervised approaches.