Applications of Deep Learning to Neuro-Imaging Techniques
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in var...
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2019-08-01
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doaj-a026d9d98d6f482795a6fb0163fe38822020-11-24T21:12:38ZengFrontiers Media S.A.Frontiers in Neurology1664-22952019-08-011010.3389/fneur.2019.00869459874Applications of Deep Learning to Neuro-Imaging TechniquesGuangming ZhuBin JiangLiz TongYuan XieGreg ZaharchukMax WintermarkMany clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.https://www.frontiersin.org/article/10.3389/fneur.2019.00869/fullartificial intelligencedeep learningradiologyneuro-imagingacquisition |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guangming Zhu Bin Jiang Liz Tong Yuan Xie Greg Zaharchuk Max Wintermark |
spellingShingle |
Guangming Zhu Bin Jiang Liz Tong Yuan Xie Greg Zaharchuk Max Wintermark Applications of Deep Learning to Neuro-Imaging Techniques Frontiers in Neurology artificial intelligence deep learning radiology neuro-imaging acquisition |
author_facet |
Guangming Zhu Bin Jiang Liz Tong Yuan Xie Greg Zaharchuk Max Wintermark |
author_sort |
Guangming Zhu |
title |
Applications of Deep Learning to Neuro-Imaging Techniques |
title_short |
Applications of Deep Learning to Neuro-Imaging Techniques |
title_full |
Applications of Deep Learning to Neuro-Imaging Techniques |
title_fullStr |
Applications of Deep Learning to Neuro-Imaging Techniques |
title_full_unstemmed |
Applications of Deep Learning to Neuro-Imaging Techniques |
title_sort |
applications of deep learning to neuro-imaging techniques |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neurology |
issn |
1664-2295 |
publishDate |
2019-08-01 |
description |
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques. |
topic |
artificial intelligence deep learning radiology neuro-imaging acquisition |
url |
https://www.frontiersin.org/article/10.3389/fneur.2019.00869/full |
work_keys_str_mv |
AT guangmingzhu applicationsofdeeplearningtoneuroimagingtechniques AT binjiang applicationsofdeeplearningtoneuroimagingtechniques AT liztong applicationsofdeeplearningtoneuroimagingtechniques AT yuanxie applicationsofdeeplearningtoneuroimagingtechniques AT gregzaharchuk applicationsofdeeplearningtoneuroimagingtechniques AT maxwintermark applicationsofdeeplearningtoneuroimagingtechniques |
_version_ |
1716750354865979393 |