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Annual International Symposium on Information Management and Big Data

SIMBig 2019: Information Management and Big Data pp 76–88Cite as

TensorFlow for Doctors

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1070))

Abstract

Machine learning has advanced substantially in the past few years, and there are many generic solutions freely available to classify text and images. The solutions are so straightforward to set up and run that having a software background is no longer necessary to perform machine learning experimentation. These systems are being adapted in many ways, and it seems only natural that those in the medical field may wish to see how machine learning might help with their research. This research examines if off-the-shelf machine learning systems are suitable for research by medical professionals who do not have software backgrounds. If all doctors who wish to experiment with machine learning could have an adequate system available, the impact on research could be substantial. This investigation applies a commonly available machine learning practice lab to medical images. As part of this investigation, we evaluated the TensorFlow for Poets (TFP) tutorial from Google Code Labs with openly available medical images provided by Kaggle Inc. While we would not recommend our test results as a basis for diagnosing medical conditions, the results were encouraging enough to suggest that using off-the-shelf systems can offer a promising opportunity to expand machine learning research into those with medical, but not software backgrounds.

Supported by the University of St. Thomas.

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Author information

Authors and Affiliations

  1. University of St. Thomas, St. Paul, MN, 55105, USA

    Isha Agarwal, Rajkumar Kolakaluri & Michael Dorin

  2. Universidad del Pacífico, Lima, Peru

    Mario Chong

Authors
  1. Isha Agarwal
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  2. Rajkumar Kolakaluri
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  3. Michael Dorin
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  4. Mario Chong
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Corresponding author

Correspondence to Michael Dorin .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. University of A Coruña, A Coruña, Spain

    Nelly Condori-Fernandez

  3. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

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Agarwal, I., Kolakaluri, R., Dorin, M., Chong, M. (2020). TensorFlow for Doctors. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-46140-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46139-3

  • Online ISBN: 978-3-030-46140-9

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