For the busy clinical-imaging professional in an AI world: Gaining intuition about deep learning without math

Abstract

Medical diagnostics comprise recognizing patterns in images, tissue slides, and symptoms. Deep learning algorithms (DLs) are well suited to such tasks, but they are black boxes in various ways. To explain DL Computer-Aided Diagnostic (CAD) results and their accuracy to patients, to manage or drive the direction of future medical DLs, to make better decisions with CAD, etc., clinical professionals may benefit from hands-on, under-the-hood lessons about medical DL. For those who already have some high-level knowledge about DL, the next step is to gain a more-fundamental understanding of DLs, which may help illuminate inside the boxes. The objectives of this Continuing Medical Education (CME) article include:

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  Gain an intuitive understanding of DL
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  Learn example DL architectures for pattern recognition
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  Explore optimization of DLs and some factors that affect their accuracy using non-clinical images as input data
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  Gain insights from simulations to probe DL behavior and options to optimize DLs
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  Review lessons learned from the simulations

Better understanding can come from relatable medical analogies and personally experiencing quick simulations to observe deep learning in action, akin to the way clinicians are trained to perform other tasks. We developed readily-implementable demonstrations and simulation exercises. We framed the exercises using analogies to breast cancer, malignancy and cancer stage as example diagnostic applications. The simulations revealed a nuanced relationship between DL output accuracy and the quantity and nature of the data. The simulation results provided lessons-learned and implications for the clinical world. Although we focused on DLs for diagnosis, they are similar to DLs for treatment (e.g. radiotherapy) so that treatment providers may also benefit from this tutorial.