Abstract

Info

Author(s):: LUO Siyan1; WANG Xinzhou2; RAO Xiangrong1; 1. Department of Nephropathy, Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China 2. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China

Keywords:: Keywords: self-supervised learning tongue diagnosis image clustering Gaussian mixture model

Abstract:: Abstract: Objective To solve the problem of high cost of data annotation and evident subjectivity in the artificial intelligence-based tongue diagnosis. Methods The proposed method adopted the contrastive learning of deep learning for realizing the self-supervised clustering of tongue images. The convolutional neural network was used to map tongue images under different data enhancement patterns into the latent space, and to distinguish non-similar instances as much as possible while learning the common features among similar instances. The feature vectors extracted by the network were subsequently clustered using Gaussian mixture model. Results A clustering accuracy of 53% was obtained using 300 unlabeled images collected by the instrument for tongue image analysis without introducing prior knowledge. Conclusion The method somewhat frees medical practitioners from the laborious and tedious task of data labeling. In addition to its application to automated tongue classification, the method can further be used for the clustering analysis of special tongue syndromes of different diseases and its extracted tongue features can provide pre-trained references for downstream tasks such as tongue segmentation, tongue color classification, and moss texture partitioning.