https://scholars.lib.ntu.edu.tw/handle/123456789/597676
標題: | Distance Ordering: A Deep Supervised Metric Learning for Pain Intensity Estimation | 作者: | Ting, Jie Yang, Yi Cheng LI-CHEN FU CHU-LIN TSAI CHIEN-HUA HUANG |
關鍵字: | Deep learning | Facial Expression | Pain | Video Recognition | Visual Analogue Scale | 公開日期: | 1-一月-2021 | 起(迄)頁: | 1083-1088 | 來源出版物: | Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 | 會議論文: | 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 | 摘要: | The pain intensity level is one of the properties to know the patients' status. In this work, we focus on estimating each individual's self-reported pain metric called Visual Analogue Scale (VAS), which is considered the standard gold measurement in the triage system. The VAS pain score is highly subjective, and its range may vary significantly among different patients. To tackle these issues, we designed an end-to-end training deep learning model for the automatic measurement of VAS based on video facial recognition. We proposed a novel loss method named Distance Ordering. By using Distance Ordering, we can extract the features with ordinal meaning according to the ordinal relationship of pain intensity levels. Experimental results on the UNBC-McMaster Pain Archive Database benchmark show that the model we designed outperforms the other previous works and achieves the state-of-the-art performance with Mean Square Error (MSE), Mean Absolute Error (MAE), Intra-class correlation (ICC), and Pearson coefficient correlation (PCC). Also, the ablation studies demonstrate that our approach can improve the VAS estimation. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/597676 https://api.elsevier.com/content/abstract/scopus_id/85125878665 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125878665&doi=10.1109%2fICMLA52953.2021.00177&partnerID=40&md5=429cb341857070f106a2b93db21e6728 |
ISBN: | 9781665443371 | DOI: | 10.1109/ICMLA52953.2021.00177 |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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