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Calibrated Bayesian neural networks to estimate gestational age and its uncertainty on fetal brain ultrasound images

Abstract:

We present an original automated framework for estimating gestational age (GA) from fetal ultrasound head biometry plane images. A novelty of our approach is the use of a Bayesian Neural Network (BNN), which quantifies uncertainty of the estimated GA. Knowledge of estimated uncertainty is useful in clinical decision-making, and is especially important in ultrasound image analysis where image appearance and quality can naturally vary a lot. A further novelty of our approach is that the neural ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-60334-2_2

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-3060-3772
Publisher:
Springer Publisher's website
Pages:
13-22
Series:
Lecture Notes in Computer Science
Series number:
12437
Host title:
Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis
Publication date:
2020-10-01
Acceptance date:
2020-07-28
Event title:
ASMUS: International Workshop on Advances in Simplifying Medical Ultrasound 2020
Event location:
Lima, Peru
Event website:
https://sites.google.com/view/asmus2020
Event start date:
2020-10-04T00:00:00Z
Event end date:
2020-10-04T00:00:00Z
DOI:
EISBN:
9783030603342
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783030603335
Language:
English
Keywords:
Pubs id:
1139805
Local pid:
pubs:1139805
Deposit date:
2020-12-01

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