Advances in deep learning for brain glioma imaging(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第8期
- Page:
- 1048-1052
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Advances in deep learning for brain glioma imaging
- Author(s):
- ZHANG Bin; XUE Caiqiang; LIN Xiaoqiang; JING Mengyuan; DENG Liangna; HAN Tao; ZHOU Junlin
- Department of Radiology, Lanzhou University Second Hospital/the Second Clinical Medical School, Lanzhou University/Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China
- Keywords:
- Keywords: deep learning glioma imaging review
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2021.08.025
- Abstract:
- Abstract: Deep learning is a kind of deep network that can discover the inherent complex features of data based on a multi-layer neural network computing model. It is mostly used in the segmentation and classification of medical images, and it also makes lots of achievements in the research of various types of gliomas. Herein the applications of deep learning in the accurate segmentation and positioning, prediction of tissue genetic features, and prognostic evaluation of brain gliomas are reviewed, and the recent advances in deep learning for image segmentation and classification of gliomas are summarized, so as to provide new ideas for the accurate diagnosis and individualized treatment of glioma patients.
Last Update: 2021-07-31