Aweakly supervised tooth-mark and crack detectionmethod in tongue image

摘要

Tongue diagnosis is one of the primary clinical diagnostic methods in Traditional Chinese Medicine. Recognizing the tooth-marked tongue and the crackled tongue plays an essential role in evaluating the status of patients. Previous methods mainly focus on identifying whether a tongue image is a tooth-marked tongue (cracked tongue) or not, while cannot provide more details. In this study, we propose a weakly supervised method for training the tooth-mark and crack detection model by leveraging fully bounding-box level annotated and coarse image-level annotated tongue images. The proposed model is extended from the YOLO object detection model, and we add several classification branches for recognizing the tooth-marked tongue and cracked tongue. The classification branch aims to predict the coarse label for both coarse-labeled data and fully annotated data. The detection branch is used to locate the position of tooth marks and cracks from the fully annotated data. Finally, we utilize a multitask loss function for training the model. Experimental results on a challenging tongue image dataset demonstrate the effectiveness of our proposed weakly supervised method.