A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees. - Wiggins, Walter F.; Caton Jr, M. Travis; Magudia, Kirti; Rosenthal, Michael H.; Andriole, Katherine P. (Journal of Digital Imaging, 2021)
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.