Abstract
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: this https URL
Abstract (translated)
在心超图像中实现自动视角分类可以帮助资源不足的诊所和医院，在专业技术人员可能不可用的情况下，提供更快的诊断和筛查。我们提出了一种新颖的方法来解决心超图视角分类问题。研究表明，将视角分类视为视频分类而非图像分类可以带来优势。为此，我们提出了一个结合了CNN（卷积神经网络）与GRU（门控循环单元）架构，并引入了一种新的时间特征编织方法，这种方法利用空间和时间信息，在仅使用连续四帧的情况下，相比于基线图像分类，准确率提高了4.33%。此外，我们的方法对计算资源的要求非常低。 我们还发布了新生儿心超数据集（NED），这是一个由专业人员标注的数据集，包含十六个视角及其相关的心脏超声视频，以鼓励未来在这个领域的研究和开发工作。代码可在以下链接获取：this https URL
URL
https://arxiv.org/abs/2501.03967
