TensorFlow for Doctors

Isha Agarwal, Rajkumar Kolakaluri, Michael Dorin, Mario Chong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


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.
Original languageEnglish
Title of host publicationInformation Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
EditorsJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Number of pages13
ISBN (Electronic)9783030461393
StatePublished - 1 Jan 2020
EventCommunications in Computer and Information Science -
Duration: 1 Jan 2020 → …

Publication series

NameCommunications in Computer and Information Science
Volume1070 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceCommunications in Computer and Information Science
Period1/01/20 → …

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.


  • Image classification
  • Machine learning
  • TensorFlow


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