Abstract: Coronary artery disease is a common clinical cardiovascular disease,and coronary intervention is one of its common treatment methods.However,diabetes mellitus is a risk factor for coronary artery disease,and the combination of diabetes mellitus and coronary artery disease significantly increases the risk of treatment,so early diagnosis and corresponding measures are of great clinical significance for these patients.Clinical indicators are important references for the diagnosis and treatment of coronary heart disease and their comorbidities,and most of these indicators are invasive.Tongue image,as an external manifestation of human health,not only reflects the tongue color,moss color and other characteristics,but also correlates with various physiological and pathological features of the heart.The development of deep learning provides assistance for objectivized and reproducible acquisition of tongue representations.However,existing tongue image classification methods are limited by the singularity of dataset labels,which leads to the lack of model generalization ability.To this end,a cross-task migration learning tongue diagnosis method M2T-Net based on multi-source data is proposed.Specifically it consists of two phases,in the pre-training phase of multi-source data,high quality image encoders under different tasks are acquired.In the cross-task migration phase,the feature representations from different tasks are fused for disease classification by combining the cross-attention mechanism.Experiments show that the performance of the M2T-Net model in the classification tasks of coronary heart disease and coronary heart disease accompanied by diabetes mellitus reaches a classification accuracy of 93%,which is better than the existing state-of-the-art methods,with strong generalization ability and practicality,and the cross-task acquisition of disease representations is more in line with the holistic diagnostic idea of Chinese medicine tongue diagnosis,which provides a more practical solution for the field of tongue image analysis.

Key words: Transfer learning, Tongue image diagnosis, Cross attention, Deep learning, Intelligent tongue diagnosis