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 MSc 4 , Paul M. Brennan MBBChir, FRCS(Ed), PhD 2 , 3 , and Jacques Fleuriot PhD 4
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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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Contributor Notes

Correspondence Michael T. C. Poon: mpoon@ed.ac.uk.

INCLUDE WHEN CITING Published online January 29, 2021; DOI: 10.3171/2020.10.JNS203834.

Disclosures The authors report no conflict of interest.

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    Raju B, Jumah F, Ashraf O, 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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    Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson; 2020.

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    Pedregosa F, Varoquaux G, Gramfort A, Scikit-learn: machine learning in Python. JMLR. 2011;12(85):28252830.

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

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

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