# Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome

arXiv: https://arxiv.org/abs/2012.03679 · HTML: https://mlsanity.com/paper/2012.03679/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound](https://mlsanity.com/paper/2407.15119/) (2024)
- [Adnexal Mass Segmentation with Ultrasound Data Synthesis](https://mlsanity.com/paper/2209.12305/) (2022)

## Abstract (arXiv)

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 and a better robustness towards initialisation compared to previous works.