Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783274412541214720 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5661078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT xueyong applicationofdeeplearninginautomatedanalysisofmolecularimagesincancerasurvey AT chenshihui applicationofdeeplearninginautomatedanalysisofmolecularimagesincancerasurvey AT qinjing applicationofdeeplearninginautomatedanalysisofmolecularimagesincancerasurvey AT liuyong applicationofdeeplearninginautomatedanalysisofmolecularimagesincancerasurvey AT huangbingsheng applicationofdeeplearninginautomatedanalysisofmolecularimagesincancerasurvey AT chenhanwei applicationofdeeplearninginautomatedanalysisofmolecularimagesincancerasurvey |