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A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees

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Abstract

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences.

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Acknowledgements

The authors would like to thank Drs. James Condon and Andreas Rauschecker for their suggestions regarding the design of the Kaggle Notebook implementation. We would also like to thank Dr. Judy Wawira Gichoya and the ACR Resident and Fellow Section for the invitation to present the module at their AI Journal Club.

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Authors and Affiliations

  1. Department of Radiology, Brigham & Women’s Hospital/Harvard Medical School, Boston, MA, USA

    Walter F. Wiggins, M. Travis Caton Jr, Kirti Magudia, Michael H. Rosenthal & Katherine P. Andriole

  2. MGH-BWH Center for Clinical Data Science, Boston, MA, USA

    Walter F. Wiggins, M. Travis Caton Jr, Kirti Magudia & Katherine P. Andriole

  3. Department of Radiology, Duke University Health System, Durham, NC, USA

    Walter F. Wiggins

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  1. Walter F. Wiggins
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  2. M. Travis Caton Jr
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  3. Kirti Magudia
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  4. Michael H. Rosenthal
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  5. Katherine P. Andriole
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Correspondence to Walter F. Wiggins.

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Wiggins, W.F., Caton, M.T., Magudia, K. et al. A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees. J Digit Imaging 34, 1026–1033 (2021). https://doi.org/10.1007/s10278-021-00492-9

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  • DOI: https://doi.org/10.1007/s10278-021-00492-9

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