Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide

Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, Eyal Klang

Research output: Contribution to journalReview articlepeer-review

268 Scopus citations

Abstract

Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are entailed in the design process of deep learning radiology research. In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional neural networks-to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. The survey of the studies is followed by a discussion about current challenges and future trends and their potential implications for radiology. This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks.

Original languageEnglish
Pages (from-to)590-606
Number of pages17
JournalRadiology
Volume290
Issue number3
DOIs
StatePublished - 1 Mar 2019
Externally publishedYes

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