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Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images

Abstract:

Convolutional neural networks (CNNs) have become the architecture of choice for visual recognition tasks. However, these models are perceived as black boxes since there is a lack of understanding of the learned behavior from the underlying task of interest. This lack of transparency is a serious drawback, particularly in applications involving medical screening and diagnosis since poorly understood model behavior could adversely impact subsequent clinical decision-making. Recently, researcher...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1117/1.jmi.5.3.034501

Authors


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ORCID:
0000-0003-0871-8634
Silamut, K More by this author
Hossain, MA More by this author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM
Subgroup:
Tropical Medicine
ORCID:
0000-0002-5355-0562
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National Library of Medicine More from this funder
National Institutes of Health More from this funder
Wellcome Trust More from this funder
Publisher:
Society of Photo-Optical Instrumentation Engineers Publisher's website
Journal:
Journal of Medical Imaging Journal website
Volume:
5
Issue:
3
Pages:
Article: 034501
Publication date:
2018-07-18
Acceptance date:
2018-06-25
DOI:
EISSN:
2329-4310
ISSN:
2329-4302
Pubs id:
pubs:896701
URN:
uri:f9b849f3-3e6c-49cd-b27d-3db782d78521
UUID:
uuid:f9b849f3-3e6c-49cd-b27d-3db782d78521
Local pid:
pubs:896701

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