用Transformer和掩码自编码器提升视频疼痛识别准确率
Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment
- 结合掩码自编码器与Transformer，捕捉面部表情与微表情
- 在AI4Pain数据集上实现更精准的疼痛等级识别
- 适合临床辅助诊断、无法言说疼痛的患者群体
准确的疼痛评估对医疗诊断和治疗至关重要；然而，依赖自我报告的传统方法对无法沟通的患者效果不佳。前沿人工智能有望通过面部视频数据支持临床疼痛识别。本文提出一种基于Transformer的深度学习模型，结合强大的掩码自编码器与Transformer分类器，有效捕捉面部表情及微表情中的疼痛指标。实验在AI4Pain数据集上进行，结果表现优异，为全面且客观的创新医疗解决方案铺平道路。
原文摘要 · Abstract (English)
Accurate pain assessment is crucial in healthcare for effective diagnosis and treatment; however, traditional methods relying on self-reporting are inadequate for populations unable to communicate their pain. Cutting-edge AI is promising for supporting clinicians in pain recognition using facial video data. In this paper, we enhance pain recognition by employing facial video analysis within a Transformer-based deep learning model. By combining a powerful Masked Autoencoder with a Transformers-based classifier, our model effectively captures pain level indicators through both expressions and micro-expressions. We conducted our experiment on the AI4Pain dataset, which produced promising results that pave the way for innovative healthcare solutions that are both comprehensive and objective.
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