Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging

Geoff Currie; K Elizabeth Hawk; Eric Rohren; Alanna Vial; Ran Klein

doi:10.1016/j.jmir.2019.09.005

Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging

J Med Imaging Radiat Sci. 2019 Dec;50(4):477-487. doi: 10.1016/j.jmir.2019.09.005. Epub 2019 Oct 7.

Authors

Geoff Currie¹, K Elizabeth Hawk², Eric Rohren³, Alanna Vial⁴, Ran Klein⁵

Affiliations

¹ Charles Sturt University, NSW, Australia. Electronic address: gcurrie@csu.edu.au.
² Stanford University, Stanford, California, USA.
³ Baylor College of Medicine, Houston, Texas, USA.
⁴ University of Wollongong, Wollongong, NSW, Australia.
⁵ University of Ottawa, Ottawa, Ontario, Canada.

PMID: 31601480
DOI: 10.1016/j.jmir.2019.09.005

Abstract

Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.

Keywords: Medical imaging; artificial intelligence; artificial neural network; convolutional neural network; deep learning.

Publication types

Review

MeSH terms

Algorithms*
Artificial Intelligence*
Deep Learning*
Diagnostic Imaging / methods*
Humans
Machine Learning*
Neural Networks, Computer*