论文标题
CP-AGCN：基于Pytorch的注意知情图形卷积网络，用于识别有脑瘫风险的婴儿
CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy
论文作者
论文摘要
早期预测在临床上被认为是脑瘫（CP）治疗的重要部分之一。我们建议实施一个基于一般运动评估（GMA）的CP预测的低成本和可解释的分类系统。我们设计了一个基于Pytorch的注意力图形卷积网络，以识别从RGB视频中提取的骨骼数据中有CP风险的早期婴儿。我们还设计了一个频率模块，用于在过滤噪声时学习频域中的CP运动。我们的系统仅需要消费级RGB视频进行培训，以通过提供可解释的CP分类结果来支持交互式时间CP预测。
Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA). We design a Pytorch-based attention-informed graph convolutional network to early identify infants at risk of CP from skeletal data extracted from RGB videos. We also design a frequency-binning module for learning the CP movements in the frequency domain while filtering noise. Our system only requires consumer-grade RGB videos for training to support interactive-time CP prediction by providing an interpretable CP classification result.
