论文标题
在端到端网络中探索基于视力的帕金森氏症的严重性评估的运动边界
Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment
论文作者
论文摘要
评估诸如帕金森氏病（PD）之类的神经系统疾病是一项艰巨的任务，需要评估几种运动和非运动功能。在本文中，我们提出了一个端到端的深度学习框架，以测量统一帕金森氏病评级量表（UPDRS）的两个重要组成部分的PD严重程度。我们的方法利用了由时间段框架训练的膨胀的3D CNN，以学习视频数据中的空间和长时间结构。我们还部署了时间关注机制来提高模型的性能。此外，将运动边界作为一种额外的输入方式探索，以帮助使摄像机运动的效果掩盖更好的运动评估。我们消除了不同数据模式对拟议网络准确性的影响，并与其他流行的体系结构进行比较。我们在25名PD患者的数据集上评估了我们的方法，分别获得了手动运动和步态任务的72.3％和77.1％的TOP-1准确性。
Evaluating neurological disorders such as Parkinson's disease (PD) is a challenging task that requires the assessment of several motor and non-motor functions. In this paper, we present an end-to-end deep learning framework to measure PD severity in two important components, hand movement and gait, of the Unified Parkinson's Disease Rating Scale (UPDRS). Our method leverages on an Inflated 3D CNN trained by a temporal segment framework to learn spatial and long temporal structure in video data. We also deploy a temporal attention mechanism to boost the performance of our model. Further, motion boundaries are explored as an extra input modality to assist in obfuscating the effects of camera motion for better movement assessment. We ablate the effects of different data modalities on the accuracy of the proposed network and compare with other popular architectures. We evaluate our proposed method on a dataset of 25 PD patients, obtaining 72.3% and 77.1% top-1 accuracy on hand movement and gait tasks respectively.
