二维婴儿姿态估计中的盲区：从带噪声标注中进行鲁棒学习

中文摘要 AI 辅助

带噪声标注对监督式深度学习构成重大挑战，因为神经网络依赖大规模高质量标注数据，这类数据的损坏会严重损害模型性能。尽管分类任务的标签噪声鲁棒性已得到广泛研究，但姿态估计（Pose Estimation, PE）领域对此的探索仍相对不足。这一局限在临床场景中尤为关键，包括新生儿科，其中早产儿的姿态估计用于支持自发性运动评估，而自发性运动是神经发育轨迹的关键指标。在这类场景中，婴儿图像的标注还因视觉挑战（如关键点自遮挡、护理人员干扰）而受阻，使标注过程本身易出错。为解决姿态估计中的带噪声标注问题，我们提出了基于训练动态记忆的可靠关键点选择方法（REliable keypoint selection via Memory of traINing Dynamics, REMIND），这是一种基于聚类的关键点选择策略，利用逐关键点的训练动态识别噪声标签，且无需假设噪声分布的任何先验知识，从而实现无噪声的模型训练。在包含46个真实临床场景下早产儿视频的专有NeoPose数据集上评估时，REMIND在多种损坏场景下均能正确识别噪声标注，在相关文献中使用的三种不同姿态估计架构下，其曲线下面积（Area Under the Curve, AUC）最高可达93%。据我们所知，这是首个明确解决早产儿姿态估计中标签噪声问题的研究，为数据质量无法保证时设计用于婴儿监测支持的可信学习算法铺平了道路。

英文摘要

Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose corruption can severely impair model performance. Although robustness to label noise has been extensively studied for classification tasks, it remains relatively underexplored in Pose Estimation (PE). This limitation becomes critical in clinical contexts, including neonatology, where PE of preterm infants is used to support the assessment of spontaneous motility, a key indicator of neurodevelopmental trajectories. In such settings, infants' images labeling is further hindered by visual challenges (e.g., keypoint self-occlusions, caregiver interference), making the annotation process inherently susceptible to errors. To tackle noisy annotations in PE, we introduce REliable keypoint selection via Memory of traINing Dynamics (REMIND), a clustering-based keypoint-selection strategy that exploits keypoint-wise training dynamics to identify noisy labels without assuming any prior knowledge of the noise distribution, thus enabling noise-free model training. When evaluated on the proprietary NeoPose dataset, comprising 46 videos of 46 preterm infants recorded in real clinical settings, REMIND correctly identifies noisy annotations across multiple corruption scenarios, achieving up to 93\% Area Under the Curve (AUC) with three different PE architectures used in the relevant literature. To our knowledge, this is the first study to explicitly address label noise in preterm infants' PE, paving the way for the design of trustworthy learning-based algorithms for infants'monitoring support when data quality cannot be guaranteed.

发表机构

• Università degli Studi “G. d’Annunzio” Chieti-Pescara（G. d'Annunzio 基耶蒂-佩斯卡拉大学）
• University of Macerata（马切拉塔大学）
• KU Leuven（鲁汶大学）
• Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
• Università di Teramo（泰拉莫大学）

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