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- Title
Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey.
- Authors
Yong Xue; Shihui Chen; Jing Qin; Yong Liu; Bingsheng Huang; Hanwei Chen
- Abstract
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 learningmodels for better performance in the applications in cancer molecular imaging.
- Publication
Contrast Media & Molecular Imaging, 2017, Vol 2017, p1
- ISSN
1555-4309
- Publication type
Article
- DOI
10.1155/2017/9512370