  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
$\alpha$-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.
