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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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  • 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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Abstract

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

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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.
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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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