融合多表情特征与自适应平衡，提升帕金森病严重程度诊断准确率
Facial Expression-based Parkinson's Disease Severity Diagnosis via Feature Fusion and Adaptive Class Balancing
- 通过注意力机制融合多种面部表情特征，增强表征能力
- 在多个帕金森病阶段数据上实现高精度分类，避免误诊
- 适合需要精细化评估病情进展的临床与科研人员
帕金森病（PD）严重程度诊断对早期发现患者和实施个性化干预至关重要。基于面部表情的诊断方法利用患者典型的‘面具脸’症状，因其便捷性和低成本而日益受到关注。然而，现有方法多依赖单一表情，易导致误诊，且忽略不同疾病阶段间的类别不平衡问题，降低预测性能。此外，多数研究仅关注二分类（即帕金森病/非帕金森病），而非评估病情严重程度。为此，本文提出一种新的基于面部表情的帕金森病严重程度诊断方法，通过注意力机制融合多种表情特征，并采用自适应类别平衡策略，动态调整样本贡献度，以应对类别分布不均和分类难度差异。实验结果表明，该方法在帕金森病严重程度诊断上表现优异，验证了注意力特征融合与自适应平衡的有效性。
原文摘要 · Abstract (English)
Parkinson's disease (PD) severity diagnosis is crucial for early detecting potential patients and adopting tailored interventions. Diagnosing PD based on facial expression is grounded in PD patients' "masked face" symptom and gains growing interest recently for its convenience and affordability. However, current facial expression-based approaches often rely on single type of expression which can lead to misdiagnosis, and ignore the class imbalance across different PD stages which degrades the prediction performance. Moreover, most existing methods focus on binary classification (i.e., PD / non-PD) rather than diagnosing the severity of PD. To address these issues, we propose a new facial expression-based method for PD severity diagnosis which integrates multiple facial expression features through attention-based feature fusion. Moreover, we mitigate the class imbalance problem via an adaptive class balancing strategy which dynamically adjusts the contribution of training samples based on their class distribution and classification difficulty. Experimental results demonstrate the promising performance of the proposed method for PD severity diagnosis, as well as the efficacy of attention-based feature fusion and adaptive class balancing.
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