Letter to the Editor. Misconceptions in the field guide to big data for neurosurgeons

Michael T. C. Poon MBChB, MSc, MRCS(Ed)1,2,3, Jorge Gaete-Villegas MSc4, Paul M. Brennan MBBChir, FRCS(Ed), PhD2,3, and Jacques Fleuriot PhD4
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  • 1 Usher Institute, University of Edinburgh, United Kingdom
  • | 2 Royal Infirmary of Edinburgh, United Kingdom
  • | 3 Cancer Research UK Brain Tumour Centre of Excellence, University of Edinburgh, United Kingdom
  • | 4 Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, United Kingdom
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Illustration from Serra et al. (pp 458–468). Copyright Uğur Türe. Published with permission.

JNS + Pediatrics - 1 year subscription bundle (Individuals Only)

USD  $515.00

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USD  $612.00
  • 1

    Raju B , Jumah F , Ashraf O , et al. Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons . J Neurosurg . Published online October 2, 2020. doi:10.3171/2020.5.JNS201288

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  • 2

    Russell S , Norvig P . Artificial Intelligence: A Modern Approach . 4th ed. Pearson ; 2020 .

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    Pedregosa F , Varoquaux G , Gramfort A , et al. Scikit-learn: machine learning in Python . JMLR . 2011 ;12 (85 ):2825 2830 .

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    Abadi M , Agarwal A , Barham P , et al. TensorFlow: large-scale machine learning on heterogeneous systems . Accessed December 29, 2020. https://arxiv.org/abs/1603.04467

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  • 5

    Paszke A , Gross S , Massa F , et al. PyTorch: an imperative style, high-performance deep learning library . Accessed December 29, 2020. https://arxiv.org/abs/1912.01703

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