基于多示例学习的颈椎健康评分方法

This paper introduces an online scoring method for cervical vertebrae health based on multiple instance learning (MIL) of multiple-valued input, in order to assess cervical vertebrae health score and solve the data labeling difficulty. It is only necessary to simply label the long-term sequence of cervical vertebrae motion data during the training phase to estimate the health score of the cervical short-term state. Firstly, the multiple-valued input is divided into sub-classifiers of multiple binary inputs and trained separately. Then use the Gaussian model to fuse the instance scores trained by each sub-classifier. Finally, the bag score is calculated with a new scoring mechanism and the cervical vertebrae health can be assessed in real-time. Qualitative and quantitative experiments include the bag score prediction accuracy, instance visualization analysis, bag score curve analysis and real-time scoring analysis, which illustrate the effectiveness of the algorithm in assessing the health of the cervical vertebrae.