Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Tan:2020:10.1007/978-3-030-60334-2_24,
author = {Tan, J and Au, A and Meng, Q and FinesilverSmith, S and Simpson, J and Rueckert, D and Razavi, R and Day, T and Lloyd, D and Kainz, B},
doi = {10.1007/978-3-030-60334-2_24},
pages = {243--252},
publisher = {Springer},
title = {Automated detection of congenital heart disease in fetal ultrasound screening},
url = {http://dx.doi.org/10.1007/978-3-030-60334-2_24},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Tan,J
AU  - Au,A
AU  - Meng,Q
AU  - FinesilverSmith,S
AU  - Simpson,J
AU  - Rueckert,D
AU  - Razavi,R
AU  - Day,T
AU  - Lloyd,D
AU  - Kainz,B
DO  - 10.1007/978-3-030-60334-2_24
EP  - 252
PB  - Springer
PY  - 2020///
SN  - 0302-9743
SP  - 243
TI  - Automated detection of congenital heart disease in fetal ultrasound screening
UR  - http://dx.doi.org/10.1007/978-3-030-60334-2_24
UR  - http://hdl.handle.net/10044/1/96814
ER  -
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