An End-to-End Traditional Chinese Medicine Constitution Assessment System Based on Multimodal Clinical Feature Representation and Fusion

Abstract

Traditional Chinese Medicine (TCM) constitution is a fundamental concept in TCM theory. It is determined by multimodal TCM clinical features which, in turn, are obtained from TCM clinical information of image (face, tongue, etc.), audio (pulse and voice), and text (inquiry) modality. The auto assessment of TCM constitution is faced with two major challenges: (1) learning discriminative TCM clinical feature representations; (2) jointly processing the features using multimodal fusion techniques. The TCM Constitution Assessment System (TCM-CAS) is proposed to provide an end-to-end solution to this task, along with auxiliary functions to aid TCM researchers. To improve the results of TCM constitution prediction, the system combines multiple machine learning algorithms such as facial landmark detection, image segmentation, graph neural networks and multimodal fusion. Extensive experiments are conducted on a four-category multimodal TCM constitution dataset, and the proposed method achieves state-of-the-art accuracy. Provided with datasets containing annotations of diseases, the system can also perform automatic disease diagnosis from a TCM perspective.