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Efficient ultrasound image analysis models with sonographer gaze assisted distillation

Abstract:

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledg...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-32251-9_43

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-2552-0964
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-4683-2606
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
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Publisher:
Springer Publisher's website
Journal:
Lecture Notes in Computer Science Journal website
Volume:
11767
Issue:
Part IV
Pages:
394-402
Host title:
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2019
Publication date:
2018-10-10
Acceptance date:
2019-06-06
Event title:
t MICCAI 2019, the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention
Event location:
Shenzhen, China
Event website:
https://www.miccai2019.org/
Event start date:
2019-10-13T00:00:00Z
Event end date:
2019-10-17T00:00:00Z
DOI:
EISBN:
9783030322519
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783030322502
Language:
English
Keywords:
Pubs id:
1077405
Local pid:
pubs:1077405
Deposit date:
2020-01-30

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