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Handbook of Big Data Privacy pp 285–303Cite as

Privacy Preserving Abnormality Detection: A Deep Learning Approach

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Abstract

The development of AI has made some major advances in recent years and its potential appears to be promising. In the healthcare sector, scientific competitions like ImageNet Large Scale Visual Recognition Challenges are providing evidence that computers can achieve human-like competence in image recognition. There are numerous computer models in medical diagnosis to help physicians. Among different models, deep learning algorithms, in particular convolutional neural networks are among the first choices for medical images analysis. This paper use one of the largest dataset of open-source musculoskeletal radiographs (MURA) for abnormality detection of thousands of musculoskeletal radiographs based on the deep learning to build models for detecting and localizing the abnormalities.

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Author information

Authors and Affiliations

  1. School of Engineering, University of Guelph, Guelph, ON, Canada

    Wenyu Han, Hadis Karimipour & Simon Yang

  2. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Amin Azmoodeh

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  1. Wenyu Han
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  2. Amin Azmoodeh
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  3. Hadis Karimipour
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  4. Simon Yang
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Correspondence to Hadis Karimipour .

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Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  2. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

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Han, W., Azmoodeh, A., Karimipour, H., Yang, S. (2020). Privacy Preserving Abnormality Detection: A Deep Learning Approach. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-38557-6_13

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