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A curriculum learning based approach to captioning ultrasound images

Abstract:

We present a novel curriculum learning approach to train a natural language processing (NLP) based fetal ultrasound image captioning model. Datasets containing medical images and corresponding textual descriptions are relatively rare and hence, smaller-sized when compared to the datasets of natural images and their captions. This fact inspired us to develop an approach to train a captioning model suitable for small-sized medical data. Our datasets are prepared using real-world ultrasound vide...

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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_8

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Jesus College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
ORCID:
0000-0002-5588-1410
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
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Publisher:
Spinger Publisher's website
Pages:
75-84
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:
2020-07-21
Event title:
MICCAI Advances in Simplifying Medical UltraSound (ASMUS) Workshop 2020
Event location:
Lima, Peru
Event website:
https://sites.google.com/view/asmus2020/asmus-2020?authuser=0
Event start date:
2020-10-04T00:00:00Z
Event end date:
2020-10-08T00:00:00Z
DOI:
EISBN:
9783030603342
ISSN:
0302-9743
ISBN:
9783030603335
Language:
English
Keywords:
Subtype:
Presentation
Pubs id:
1137927
Local pid:
pubs:1137927
Deposit date:
2020-10-16

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