论文标题
胎儿超声筛查中先天性心脏病的自动检测
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
论文作者
论文摘要
对于选定的心脏异常，具有超声检查的产前筛查可以显着降低新生儿死亡率。但是，对人类专业知识的需求，再加上大量的筛查案例，限制了实际上可实现的检测率。在本文中，我们讨论了深度学习技术的潜力，以帮助检测胎儿超声中的先天性心脏病（CHD）。我们提出了一条用于自动数据策展和分类的管道。在培训和推理期间，我们将辅助视图分类任务利用为相关心脏结构的特征。这种偏差有助于健康和CHD类别的F1得分从0.72和0.77和0.87和0.85提高。
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.
