用图模型分析面部表情动作单元，提升疼痛强度评估的准确性与可解释性。
GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation
- 将面部动作单元建模为节点，共现关系作为边，构建图结构表示。
- 在UNBC数据集上达到87.61%准确率和66.21%F1分数。
- 适合关注可解释性与精准疼痛评估的医疗AI研究者。
理解与疼痛相关的面部行为对数字健康至关重要，尤其对无法言语表达的患者而言。现有基于数据的方法在可解释性和疼痛严重程度量化方面存在局限。为此，我们提出GraphAU-Pain，利用图结构框架建模面部动作单元（AUs）及其相互关系，用于疼痛强度估计。将AUs作为图节点，共现关系作为边，更充分地刻画疼痛相关面部行为。通过关系图神经网络，该框架实现更高可解释性与显著性能提升。在公开的UNBC数据集上实验表明，GraphAU-Pain在疼痛强度估计中达到87.61%准确率和66.21%F1分数。
原文摘要 · Abstract (English)
Understanding pain-related facial behaviors is essential for digital healthcare in terms of effective monitoring, assisted diagnostics, and treatment planning, particularly for patients unable to communicate verbally. Existing data-driven methods of detecting pain from facial expressions are limited due to interpretability and severity quantification. To this end, we propose GraphAU-Pain, leveraging a graph-based framework to model facial Action Units (AUs) and their interrelationships for pain intensity estimation. AUs are represented as graph nodes, with co-occurrence relationships as edges, enabling a more expressive depiction of pain-related facial behaviors. By utilizing a relational graph neural network, our framework offers improved interpretability and significant performance gains. Experiments conducted on the publicly available UNBC dataset demonstrate the effectiveness of the GraphAU-Pain, achieving an F1-score of 66.21% and accuracy of 87.61% in pain intensity estimation.
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