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Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment

2024/09/08 by Minh-Duc Nguyen, Hyung-Jeong Yang, Nguyen, Minh-Duc +7 · 3 citations
Engineering · Medicine · Neuroscience · #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infrared Thermography in Medicine #Medical Imaging and Analysis

paper · pdf · doi:10.48550/arxiv.2409.05088

openalex publication_date 2024/09/08 · openalex created_date 2024/10/28 · openalex updated_date 2026/07/28

Abstract

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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