提出不确定性解耦与融合方法，提升姿态驱动的早期脑功能异常检测可靠性
UDF-GMA: Uncertainty Disentanglement and Fusion for General Movement Assessment
- 分离模型参数不确定性和数据噪声不确定性
- 在Pmi-GMA数据集上预测差质运动的准确率显著提升
- 适合临床场景中对可靠性要求高的自动化评估系统
全身运动评估（GMA）是一种通过定性分析全身运动来早期发现脑功能障碍的无创工具。自动化方法的发展可扩大其应用范围，但主流基于姿态的自动化GMA方法受限于高质量数据不足和姿态估计噪声，易产生不确定性，缺乏可靠度量会影响临床可信度。本文提出UDF-GMA，显式建模模型参数的信念不确定性（epistemic）和数据噪声的随机不确定性（aleatoric）。通过直接建模随机不确定性，并用贝叶斯近似估计信念不确定性，实现不确定性解耦。进一步将两类不确定性融合至嵌入的运动表征中，增强类别区分能力。在Pmi-GMA基准数据集上的大量实验表明，该方法在预测差质运动方面具有优异效果和良好泛化能力。
原文摘要 · Abstract (English)
General movement assessment (GMA) is a non-invasive tool for the early detection of brain dysfunction through the qualitative assessment of general movements, and the development of automated methods can broaden its application. However, mainstream pose-based automated GMA methods are prone to uncertainty due to limited high-quality data and noisy pose estimation, hindering clinical reliability without reliable uncertainty measures. In this work, we introduce UDF-GMA which explicitly models epistemic uncertainty in model parameters and aleatoric uncertainty from data noise for pose-based automated GMA. UDF-GMA effectively disentangles uncertainties by directly modelling aleatoric uncertainty and estimating epistemic uncertainty through Bayesian approximation. We further propose fusing these uncertainties with the embedded motion representation to enhance class separation. Extensive experiments on the Pmi-GMA benchmark dataset demonstrate the effectiveness and generalisability of the proposed approach in predicting poor repertoire.
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