A multimodal deep architecture for traditional Chinese medicine diagnosis,Concurrency and Computation: Practice and Experience

当前位置： X-MOL 学术 › Concurr. Comput. Pract. Exp. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A multimodal deep architecture for traditional Chinese medicine diagnosis
Concurrency and Computation: Practice and Experience ( IF 1.5 ) Pub Date : 2020-04-27 , DOI: 10.1002/cpe.5781
Yinglong Dai ₁ , Guojun Wang ₂ , Jianhua Dai ₁ , Oana Geman ₃

Affiliation

Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

更新日期：2020-04-27

点击分享查看原文

点击收藏

阅读更多本刊最新论文