题目/Title：PRTMTM: A Priori Regularization Method for Tooth-marked Tongue Classification

作者/Author：
                        Jingqiao Lu, Mingxuan Liu, Hong Chen

会议/Conference：ISCAS 2023

地点/Location：Monterey, CA, USA

年份/Issue Date：2023.21-25 May

页码/pages：pp.1-5

摘要/Abstract：

Tooth-marks on tongues usually indicate the weakness of the spleen and stomach in traditional Chinese medicine (TCM). Therefore, tooth-marked tongue classification is important to health diagnosis in TCM clinic. Existing classification methods usually do not use the prior knowledge such as the location and width of tooth-marks, resulting in easily misclassification of unremarkable tongues. In this paper, we propose a prior regularization tooth-marked tongue method (PRTMTM), which makes full use of the prior knowledge of the position and width of tooth-marks. With PRTMTM, the original tongue image is first segmented to obtain the tongue edge position. Then, the prior mask map of tooth-marks is obtained by corroding the tongue from edge to interior according to the tooth-mark width. Finally, with a proposed regularization method, the tooth-marked tongues are classified accurately in the training process together with the prior mask map of tooth-marks. To verify our method comprehensively, we build twenty-five sub-training sets with different number of images and label distributions. Compared with state-of-the-art methods, the accuracy of our method is improved by 4.78% on average and 10.61% on maximum, the AUC by 0.04 on average and 0.07 on maximum, and the generated heatmap can highlight tooth-marked regions.