论文标题
学习胎儿异常筛查的正常外观：应用于无监督的左心脏综合征的检测
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
论文作者
论文摘要
先天性心脏病被认为是最常见的先天性畸形群体之一，每1000美元的新生儿$ 6-11美元。在这项工作中，提出和评估了一个自动化的框架在超声筛查期间的心脏异常，并以先天性心脏病的子类别（HLHS）（HLHS）为例进行了评估和评估。我们提出了一种无监督的方法，该方法仅从临床确认的正常对照患者中学习健康解剖学。我们将许多已知的异常检测框架与基于$α$ gan网络的模型架构一起评估，并找到证据表明，所提出的模型的性能明显好于基于图像的异常检测的最先进，平均$ 0.81 $ auc \ euc \ emph {and}与先前的工作相比，对初始化的鲁棒性更高。
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a model architecture based on the $α$-GAN network and find evidence that the proposed model performs significantly better than the state-of-the-art in image-based anomaly detection, yielding average $0.81$ AUC \emph{and} a better robustness towards initialisation compared to previous works.
