Glioma grading on conventional mr images: a deep learning study with transfer learning
文献类型：期刊论文
| 作者 | Yang, Yang1; Yan, Lin-Feng1; Zhang, Xin1; Han, Yu1; Nan, Hai-Yan1; Hu, Yu-Chuan1; Hu, Bo1; Yan, Song-Lin2; Zhang, Jin1; Cheng, Dong-Liang3 |
| 刊名 | Frontiers in neuroscience
 |
| 出版日期 | 2018-11-15 |
| 卷号 | 12页码:10 |
| ISSN号 | 1662-453X |
| 关键词 | Deep learning Convolutional neural network (cnn) Transfer learning Glioma grading Magnetic resonance imaging (mri) |
| DOI | 10.3389/fnins.2018.00804 |
| 通讯作者 | Cui, guang-bin(cuigbtd@fmmu.edu.cn) ; Zhao, di(zhaodi@escience.cn) ; Wang, wen(wangwen@fmmu.edu.cn) |
| 英文摘要 | Background: accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. but previous studies on magnetic resonance imaging (mri) images were not effective enough. according to the remarkable performance of convolutional neural network (cnn) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the world health organization (who) low grade and high grade gliomas. methods: one hundred and thirteen glioma patients were retrospectively included. tumor images were segmented with a rectangular region of interest (roi), which contained about 80% of the tumor. then, 20% data were randomly selected and leaved out at patient-level as test dataset. alexnet and googlenet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, imagenet, to magnetic resonance images. the classification task was evaluated with five-fold cross-validation (cv) on patient-level split. results: the performance measures, including validation accuracy, test accuracy and test area under curve (auc), averaged from five-fold cv of googlenet which trained from scratch were 0.867, 0.909, and 0.939, respectively. with transfer learning and fine-tuning, better performances were obtained for both alexnet and googlenet, especially for alexnet. meanwhile, googlenet performed better than alexnet no matter trained from scratch or learned from pre-trained model. conclusion: in conclusion, we demonstrated that the application of cnn, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the cnns trained from scratch. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; SIGNAL INTENSITY ; BRAIN ; CLASSIFICATION ; TUMORS ; DIAGNOSIS ; MACHINE |
| WOS研究方向 | Neurosciences & Neurology |
| WOS类目 | Neurosciences |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:000450198700001 |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2374270 |
| 专题 | 计算机网络信息中心 |
| 通讯作者 | Cui, Guang-Bin; Zhao, Di; Wang, Wen |
| 作者单位 | 1.Fourth Mil Med Univ, Tangdu Hosp, Dept Radiol, Funct & Mol Imaging Key Lab Shaanxi Prov, Xian, Shaanxi, Peoples R China 2.Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China 3.Fourth Mil Med Univ, Student Brigade, Xian, Shaanxi, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Yang,Yan, Lin-Feng,Zhang, Xin,et al. Glioma grading on conventional mr images: a deep learning study with transfer learning[J]. Frontiers in neuroscience,2018,12:10. |
| APA | Yang, Yang.,Yan, Lin-Feng.,Zhang, Xin.,Han, Yu.,Nan, Hai-Yan.,...&Wang, Wen.(2018).Glioma grading on conventional mr images: a deep learning study with transfer learning.Frontiers in neuroscience,12,10. |
| MLA | Yang, Yang,et al."Glioma grading on conventional mr images: a deep learning study with transfer learning".Frontiers in neuroscience 12(2018):10. |
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来源：计算机网络信息中心
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