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Towards deep cellular phenotyping in placental histology

Abstract:

The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep convolutional neural network architectures and transfer learning, we can robustly localise and classify placental cells within five cla...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM; Big Data Institute
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM; Big Data Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Women's and Reproductive Health
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Women's and Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Women's and Reproductive Health
Role:
Author
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Funding agency for:
Ferlaino, M
Grant:
Methodology research grant (MR/M01326X/1
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Funding agency for:
Lindgren, C
Grant:
5P50HD028138-27
More from this funder
Funding agency for:
Lindgren, C
Grant:
5P50HD028138-27
More from this funder
Funding agency for:
Lindgren, C
Grant:
5P50HD028138-27
More from this funder
Funding agency for:
Lindgren, C
Grant:
5P50HD028138-27
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Journal:
Medical Imaging with Deep Learning Journal website
Host title:
1st Conference on Medical Imaging with Deep Learning (MIDL 2018)
Publication date:
2018-06-09
Acceptance date:
2018-05-15
Source identifiers:
856917
Keywords:
Pubs id:
pubs:856917
UUID:
uuid:293bd98d-218b-4d2b-aeb3-caf37dd41518
Local pid:
pubs:856917
Deposit date:
2018-06-11

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