Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey
Joint Authors
Huang, Bingsheng
Qin, Jing
Chen, Hanwei
Xue, Yong
Liu, Yong
Chen, Shihui
Source
Contrast Media & Molecular Imaging
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-15
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xue, Yong& Chen, Shihui& Qin, Jing& Liu, Yong& Huang, Bingsheng& Chen, Hanwei. 2017. Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey. Contrast Media & Molecular Imaging،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1141889
Modern Language Association (MLA)
Xue, Yong…[et al.]. Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey. Contrast Media & Molecular Imaging No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1141889
American Medical Association (AMA)
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. Contrast Media & Molecular Imaging. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1141889
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141889