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Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it ov...

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Detalles Bibliográficos
Autores principales: Xue, Yong, Chen, Shihui, Qin, Jing, Liu, Yong, Huang, Bingsheng, Chen, Hanwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661078/
https://www.ncbi.nlm.nih.gov/pubmed/29114182
http://dx.doi.org/10.1155/2017/9512370
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author Xue, Yong
Chen, Shihui
Qin, Jing
Liu, Yong
Huang, Bingsheng
Chen, Hanwei
author_facet Xue, Yong
Chen, Shihui
Qin, Jing
Liu, Yong
Huang, Bingsheng
Chen, Hanwei
author_sort Xue, Yong
collection PubMed
description Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically. Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction. We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging.
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spelling pubmed-56610782017-11-07 Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey Xue, Yong Chen, Shihui Qin, Jing Liu, Yong Huang, Bingsheng Chen, Hanwei Contrast Media Mol Imaging Review Article Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically. Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction. We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging. Hindawi 2017-10-15 /pmc/articles/PMC5661078/ /pubmed/29114182 http://dx.doi.org/10.1155/2017/9512370 Text en Copyright © 2017 Yong Xue et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Xue, Yong
Chen, Shihui
Qin, Jing
Liu, Yong
Huang, Bingsheng
Chen, Hanwei
Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
title Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
title_full Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
title_fullStr Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
title_full_unstemmed Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
title_short Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
title_sort application of deep learning in automated analysis of molecular images in cancer: a survey
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5661078/
https://www.ncbi.nlm.nih.gov/pubmed/29114182
http://dx.doi.org/10.1155/2017/9512370
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