Convolutional herbal prescription building method from multi-scale facial features
This work addresses a novel problem in TCM diagnosis by automating prescription generation from facial images, though it appears incremental in applying existing deep learning methods to a new domain.
The authors tackled the problem of generating Traditional Chinese Medicine (TCM) herbal prescriptions from facial features, which had not been previously explored, and found that their multi-scale convolutional neural network performed better in learning relevant information from faces.
In Traditional Chinese Medicine (TCM), facial features are important basis for diagnosis and treatment. A doctor of TCM can prescribe according to a patient's physical indicators such as face, tongue, voice, symptoms, pulse. Previous works analyze and generate prescription according to symptoms. However, research work to mine the association between facial features and prescriptions has not been found for the time being. In this work, we try to use deep learning methods to mine the relationship between the patient's face and herbal prescriptions (TCM prescriptions), and propose to construct convolutional neural networks that generate TCM prescriptions according to the patient's face image. It is a novel and challenging job. In order to mine features from different granularities of faces, we design a multi-scale convolutional neural network based on three-grained face, which mines the patient's face information from the organs, local regions, and the entire face. Our experiments show that convolutional neural networks can learn relevant information from face to prescribe, and the multi-scale convolutional neural networks based on three-grained face perform better.