用于新生儿超声心动图视点视频分类的时间特征编织
Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification
AI总结：
本文提出带时间特征编织的 CNN-GRU 视频分类架构，仅用四帧即提升新生儿超声心动图视点分类准确率 4.33%，并发布含16个视点的专业标注 NED 数据集。
摘要：
超声心动图中的自动视点分类可以帮助资源不足的诊所和医院在可能没有专业技术人员时提供更快的诊断和筛查。我们提出了一种用于超声心动图视点分类的新颖方法。我们表明，将视点分类视为视频分类而非图像分类具有优势。我们提出了一种带有新颖时间特征编织方法的 CNN-GRU 架构，该方法利用空间和时间信息，仅使用四个连续帧就比基线图像分类带来 4.33% 的准确率提升。所提方法产生的计算开销极小。此外，我们发布了 Neonatal Echocardiogram Dataset（NED），这是一个经过专业标注的数据集，提供十六个视点及相关超声心动图视频，以鼓励该领域未来的工作和发展。代码可在 https://github.com/satchelfrench/NED 获取。
英文摘要：
Automated viewpoint classification in echocardiograms can help under-resourced clinics and hospitals in providing faster diagnosis and screening when expert technicians may not be available. We propose a novel approach towards echocardiographic viewpoint classification. We show that treating viewpoint classification as video classification rather than image classification yields advantage. We propose a CNN-GRU architecture with a novel temporal feature weaving method, which leverages both spatial and temporal information to yield a 4.33\% increase in accuracy over baseline image classification while using only four consecutive frames. The proposed approach incurs minimal computational overhead. Additionally, we publish the Neonatal Echocardiogram Dataset (NED), a professionally-annotated dataset providing sixteen viewpoints and associated echocardipgraphy videos to encourage future work and development in this field. Code available at: https://github.com/satchelfrench/NED
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
