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

By Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen. 2017

52 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

• Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI (2018, methodology): “The accurate automated detection of a cerebral tumour, as presented in our study, is clinically relevant as it allows for a preselection of lesions and patients of priority and as a control mechanism for the radiologist as a computer-assisted device [13, 20, 36 – 39].”