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Discovery of rare phenotypes in cellular images using weakly supervised deep learning

Abstract:

High-throughput microscopy generates a massive amount of images that enables the identification of biological phenotypes resulting from thousands of different genetic or pharmacological perturbations. However, the size of the data sets generated by these studies makes it almost impossible to provide detailed image annotations, e.g. by object bounding box. Furthermore, the variability in cellular responses often results in weak phenotypes that only manifest in a subpopulation of cells. To over...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1109/ICCVW.2017.13

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Arias-Garcia, M More by this author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Ludwig Institute More from this funder
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Grant:
Sir Henry Wellcome Postdoctoral Fellowship
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2018-01-05
Acceptance date:
2017-07-17
DOI:
ISSN:
2473-9936
Pubs id:
pubs:829443
URN:
uri:a18b5872-fffa-4e7e-901d-1612732d7e0b
UUID:
uuid:a18b5872-fffa-4e7e-901d-1612732d7e0b
Local pid:
pubs:829443

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