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- W4287597868 abstract "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." @default.
- W4287597868 created "2022-07-25" @default.
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- W4287597868 date "2020-11-15" @default.
- W4287597868 modified "2023-10-17" @default.
- W4287597868 title "Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome" @default.
- W4287597868 doi "https://doi.org/10.48550/arxiv.2012.03679" @default.
- W4287597868 hasPublicationYear "2020" @default.
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