计算机视觉的拯救：婴儿姿势对称性的估计，从不一致的注释
Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations
摘要：
双侧姿势对称性作为自闭症谱系障碍（ASD）的潜在风险标志物和婴儿先天性肌性斜颈（CMT）的症状起着关键作用，但目前评估对称性的方法需要费力的临床专家评估。在本文中，我们开发了一个计算机视觉为基础的婴儿对称性评估系统，利用三维人体姿势估计婴儿。我们的系统对地面实况评估的评估和校准是复杂的，我们的调查结果，从人类评级的角度和对称性，这种评级表现出较低的评分者间的可靠性。为了纠正这一点，我们开发了一个贝叶斯估计的地面真相来自一个概率图形模型的易犯错误的人类评分。我们发现，3D婴儿姿势估计模型可以实现68%的面积下的接收器工作特征曲线的性能预测贝叶斯聚合标签，相比之下，只有61%的2D婴儿姿势估计模型和60%的3D成人姿势估计模型，突出了3D姿势和婴儿领域的知识在评估婴儿身体对称性的重要性。我们的调查分析还表明，人类评级容易受到更高水平的偏见和不一致性，因此我们最终的3D姿势为基础的对称性评估系统进行校准，但不直接监督贝叶斯汇总人类评级，产生更高水平的一致性和更低水平的肢体间评估偏差。
英文摘要：
Bilateral postural symmetry plays a key role as a potential risk marker for autism spectrum disorder (ASD) and as a symptom of congenital muscular torticollis (CMT) in infants, but current methods of assessing symmetry require laborious clinical expert assessments. In this paper, we develop a computer vision based infant symmetry assessment system, leveraging 3D human pose estimation for infants. Evaluation and calibration of our system against ground truth assessments is complicated by our findings from a survey of human ratings of angle and symmetry, that such ratings exhibit low inter-rater reliability. To rectify this, we develop a Bayesian estimator of the ground truth derived from a probabilistic graphical model of fallible human raters. We show that the 3D infant pose estimation model can achieve 68% area under the receiver operating characteristic curve performance in predicting the Bayesian aggregate labels, compared to only 61% from a 2D infant pose estimation model and 60% from a 3D adult pose estimation model, highlighting the importance of 3D poses and infant domain knowledge in assessing infant body symmetry. Our survey analysis also suggests that human ratings are susceptible to higher levels of bias and inconsistency, and hence our final 3D pose-based symmetry assessment system is calibrated but not directly supervised by Bayesian aggregate human ratings, yielding higher levels of consistency and lower levels of inter-limb assessment bias.
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