Deep Learning in Medical Imaging and Radiation Therapy

Overview

Journal Med Phys

Specialty Biophysics

Date 2018 Oct 28

PMID 30367497

Citations 211

Authors

Berkman Sahiner

Aria Pezeshk

Lubomir M Hadjiiski

Xiaosong Wang

Karen Drukker

Kenny H Cha

Ronald M Summers

Maryellen L Giger

Affiliations

Soon will be listed here.

Abstract

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

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