Knowledge Commons of Institute of Automation,CAS
A High-Powered Brain Age Prediction Model Based On Convolutional Neural Network | |
Rao, Guangxiang1,2![]() ![]() ![]() | |
2020-03 | |
会议名称 | 17th IEEE International Symposium on Biomedical Imaging |
会议日期 | 2020年4月3日-2020年4月7日 |
会议地点 | Iowa, USA |
摘要 | Predicting individual chronological age based on neuroimaging data is very promising and important for understanding the trajectory of normal brain development. In this work, we proposed a new model to predict brain age ranging from 12 to 30 years old, based on structural magnetic resonance imaging and a deep learning approach with reduced model complexity and computational cost. We found that this model can predict brain age accurately not only in the training set (N = 1721, mean absolute error is 1.89 in 10-fold cross validation) but in an independent validation set (N = 226, mean absolute error is 1.96), substantially outperforming the previous published models. Given the considerable accuracy and generalizability, it is promising to further deploy our model in the clinic and help to investigate the pathophysiology of neurodevelopmental disorders. |
关键词 | Brain age prediction, structural magnetic resonance image, Convolutional Neural Network, Inception Net, global average pooling |
学科门类 | 工学 ; 工学::控制科学与工程 |
DOI | 10.1109/ISBI45749.2020.9098376 |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 脑网络分析 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39582 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Liu, Bing |
作者单位 | 1.Brainnetome center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Rao, Guangxiang,Li, Ang,Liu, Yong,et al. A High-Powered Brain Age Prediction Model Based On Convolutional Neural Network[C],2020. |
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