  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.
