Publications

BibTex format

@article{Chotzoglou:2021,
author = {Chotzoglou, E and Day, T and Tan, J and Matthew, J and Lloyd, D and Razavi, R and Simpson, J and Kainz, B},
journal = {Journal of Machine Learning for Biomedical Imaging},
pages = {1--25},
title = {Learning normal appearance for fetal anomaly screening: application to the unsupervised detection of Hypoplastic Left Heart Syndrome},
url = {https://www.melba-journal.org/article/27648},
volume = {2021},
year = {2021}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 new 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.
AU  - Chotzoglou,E
AU  - Day,T
AU  - Tan,J
AU  - Matthew,J
AU  - Lloyd,D
AU  - Razavi,R
AU  - Simpson,J
AU  - Kainz,B
EP  - 25
PY  - 2021///
SP  - 1
TI  - Learning normal appearance for fetal anomaly screening: application to the unsupervised detection of Hypoplastic Left Heart Syndrome
T2  - Journal of Machine Learning for Biomedical Imaging
UR  - https://www.melba-journal.org/article/27648
UR  - http://hdl.handle.net/10044/1/96711
VL  - 2021
ER  -

Download

DR BERNHARD KAINZ

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)