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Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

View ORCID ProfileVladimir Iglovikov, View ORCID ProfileAlexander Rakhlin, View ORCID ProfileAlexandr A. Kalinin, View ORCID ProfileAlexey Shvets
doi: https://doi.org/10.1101/234120
Vladimir Iglovikov
1Lyft Inc., San Francisco, CA 94107, USA
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  • ORCID record for Vladimir Iglovikov
  • For correspondence: iglovikov@gmail.com
Alexander Rakhlin
2Neuromation OU, Tallin, 10111 Estonia
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  • For correspondence: rakhlin@neuromation.io
Alexandr A. Kalinin
3University of Michigan, Ann Arbor, MI 48109, USA
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  • For correspondence: akalinin@umich.edu
Alexey Shvets
4Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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  • For correspondence: shvets@mit.edu
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Article Information

doi 
https://doi.org/10.1101/234120
History 
  • June 20, 2018.

Article Versions

  • Version 1 (December 14, 2017 - 06:02).
  • Version 2 (December 14, 2017 - 14:41).
  • Version 3 (December 15, 2017 - 07:46).
  • You are viewing Version 4, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Vladimir Iglovikov1,
  2. Alexander Rakhlin2,
  3. Alexandr A. Kalinin3 and
  4. Alexey Shvets4
  1. 1Lyft Inc., San Francisco, CA 94107, USA iglovikov{at}gmail.com
  2. 2Neuromation OU, Tallin, 10111 Estonia rakhlin{at}neuromation.io
  3. 3University of Michigan, Ann Arbor, MI 48109, USA akalinin{at}umich.edu
  4. 4Massachusetts Institute of Technology, Cambridge, MA 02142, USA shvets{at}mit.edu
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Posted June 20, 2018.
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Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey Shvets
bioRxiv 234120; doi: https://doi.org/10.1101/234120
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Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey Shvets
bioRxiv 234120; doi: https://doi.org/10.1101/234120

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