Semi-supervised learning for tongue constitution recognition

Constitution recognition based on tongue images plays an important role in the prevention and treatment of diseases in Traditional Chinese Medicine (TCM). In order to solve the problem that the tongue images with constitution labels are limited, a semi-supervised learning (SSL) method is introduced in this paper with a large number of unlabeled tongue images assisting the training of the model. In addition, focal loss is introduced by assigning different loss weights to different samples in order to tackle the unbalanced distribution of the dataset. Furthermore, the attention mechanism in both channel and spatial dimensions is also added in the process of feature extraction. Experiments results showed that our method performed best in Macro Precision, Macro Recall, and Macro F1 than other methods. The Accuracy of our method was 2.6 percentages higher than the method trained with only labeled samples. The experiments verified the effectiveness of our method.