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Cited by 1,115Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical ImagingAI in cancer detection

Deep Learning: A Primer for Radiologists

Chartrand, Gabriel, Cheng, Phillip M., Vorontsov, Eugene, Drozdzal, Michal, Turcotte, Simon, Pal, Christopher J., Kadoury, Samuel, Tang, An·2017·RadioGraphics
DOI 10.1148/rg.2017170077
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Abstract

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. ©RSNA, 2017

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  • Modern deep learning models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data.

    “Modern DL models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data2–5.”

    From The future of digital health with federated learning (2020) · cites this paper as [3]

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Authors
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A.
Affiliations
University of Southern California (US)
ORCID
Venue
RadioGraphics
Publisher
Radiological Society of North America (RSNA)
Published
2017-11
Volume
37
Issue
7
Pages
2113-2131
ISSN
  • 0271-5333 (ISSN-L)
  • 1527-1323
DOI
10.1148/rg.2017170077
OpenAlex
W2767236661
Cited by
1,115
References
7
Citations by year
2026: 75 · 2025: 90 · 2024: 150 · 2023: 198 · 2022: 146 · 2021: 163 · 2020: 137 · 2019: 112 · 2018: 42 · 2017: 1
FWCI
2.19
Type
journal article
Language
en
Open access
closed
Locations
  • Radiographics (publishedVersion)
  • PubMed (publishedVersion)
  • PolyPublie (École Polytechnique de Montréal) (submittedVersion)
Funders
MITACS (IT05356)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
deep learning, convolutional neural networks, artificial neural networks, diagnostic imaging, computer-aided diagnosis, image classification, lesion detection, image segmentation, backpropagation, clinical implementation
MeSH terms
Machine Learning, Algorithms, Humans, Image Processing, Computer-Assisted, Learning, Radiology, Radiology Information Systems, Neural Networks, Computer

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