Abstract
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.
Translated title of the contribution | Cervical Vertebrae Health Score Method Based on Multiple Instance Learning |
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Original language | Chinese (Traditional) |
Pages (from-to) | 94-103 |
Number of pages | 10 |
Journal | Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics |
Volume | 31 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2019 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Graphics and Computer-Aided Design
Keywords
- Bag scoring mechanism
- Cervical vertebrae health impact score assessment
- Multiple instance learning
- Multiple-valued input labels