Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment
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arXiv
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| Format: | Preprint |
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2024
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| author | Nguyen, Minh-Duc Yang, Hyung-Jeong Kim, Soo-Hyung Shin, Ji-Eun Kim, Seung-Won |
| author_facet | Nguyen, Minh-Duc Yang, Hyung-Jeong Kim, Soo-Hyung Shin, Ji-Eun Kim, Seung-Won |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment Nguyen, Minh-Duc Yang, Hyung-Jeong Kim, Soo-Hyung Shin, Ji-Eun Kim, Seung-Won Computer Vision and Pattern Recognition 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. |
| title | Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.05088 |