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Ultrasound image representation learning by modeling sonographer visual attention

Abstract:

Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural n...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1007/978-3-030-20351-1_46

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Balliol College
Role:
Author
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Publisher:
Springer Publisher's website
Journal:
Information Processing in Medical Imaging Journal website
Host title:
Information Processing in Medical Imaging
Publication date:
2019-05-22
Acceptance date:
2019-02-26
Event location:
The Hong Kong University of Science and Technology (HKUST)
DOI:
Source identifiers:
980414
Keywords:
Pubs id:
pubs:980414
UUID:
uuid:a27fe42b-3a94-4b0f-bc7d-2173c0348b6f
Local pid:
pubs:980414
Deposit date:
2019-03-07

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