Deep Learning for Chest Radiology: A Review

Background Compared to classical computer-aided analysis, deep learning and in particular deep convolutional neural network demonstrates breakthrough performance in many of the sophisticated chest-imaging analysis tasks, and also enables solving new problems that are infeasible to traditional machin...

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Bibliographic Details
Published inCurrent radiology reports (Philadelphia, PA ) Vol. 7; no. 8; pp. 1 - 9
Main Authors Feng, Yeli, Teh, Hui Seong, Cai, Yiyu
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2019
Springer Nature B.V
Subjects
Artificial neural networks
CAI
Chest
Chest Imaging
Chest Imaging (H Teh
Computed tomography
Computer assisted instruction
Deep learning
Feasibility studies
Imaging
Machine learning
Medicine
Medicine & Public Health
Radiographs
Radiography
Radiology
Section Editor
Deep learning
Radiography
Computed tomography
Pulmonary
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Summary:Background Compared to classical computer-aided analysis, deep learning and in particular deep convolutional neural network demonstrates breakthrough performance in many of the sophisticated chest-imaging analysis tasks, and also enables solving new problems that are infeasible to traditional machine learning. Recent Findings Deep learning application for radiology has shown that its performance for triaging adult chest radiography has reached a clinically acceptable level, while lung nodule detection from computed tomography has achieved interobserver variability comparable to experienced human observers, and automatically generating text report for chest radiograph is feasible. Summary This article will provide a review of leading and emerging deep-learning-based applications in chest radiology.
ISSN:2167-4825
2167-4825
DOI:10.1007/s40134-019-0333-9