中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共4条，第1-4条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets 期刊论文
CURRENT ALZHEIMER RESEARCH, 2020, 卷号: 17, 期号: 13, 页码: 1221-1231
作者:  Wei, Yuanyuan;  Huang, Nianwei;  Liu, Yong;  Zhang, Xi;  Wang, Silun;  Tang, Xiaoying
收藏  |  浏览/下载：175/0  |  提交时间：2021/04/21
Alzheimer's disease  mild cognitive impairment  shape analysis  MMSE  large deformation diffeomorphic metric mapping  multiple datasets  
Real-Time Orthophoto Mosaicing on Mobile Devices for Sequential Aerial Images with Low Overlap 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 22, 页码: 15
作者:  Zhao, Yong;  Cheng, Yuqi;  Zhang, Xishan;  Xu, Shibiao;  Bu, Shuhui;  Jiang, Hongkai;  Han, Pengcheng;  Li, Ke;  Wan, Gang
收藏  |  浏览/下载：244/0  |  提交时间：2021/01/06
aerial images  DOM  low overlap  mosaicing  georeferenced  orthophoto  
The Efficacy of COGnitive tRaining in patiEnts with Amnestic mild coGnitive impairmENT (COG-REAGENT): Protocol for a Multi-Center Randomized Controlled Trial 期刊论文
JOURNAL OF ALZHEIMERS DISEASE, 2020, 卷号: 75, 期号: 3, 页码: 779-787
作者:  Xing, Yi;  Zhu, Zude;  Du, Yifeng;  Zhang, Junjian;  Qu, Qiumin;  Sun, Li;  Li, Yang;  Guo, Yanjun;  Peng, Guoping;  Liu, Yong;  Yu, Yueyi;  Qiao, Yuchen;  Xie, Beijia;  Shi, Xinrui;  Lu, Jie;  Jia, Jianping;  Tang, Yi
收藏  |  浏览/下载：244/0  |  提交时间：2020/08/03
Cognitive training  mild cognitive impairment  non-pharmacological intervention  randomized controlled clinical trial  
Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks 期刊论文
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 卷号: 24, 期号: 5, 页码: 1321-1332
作者:  Niu, Jinghao;  Tang, Yongqiang;  Sun, Zhengya;  Zhang, Wensheng
收藏  |  浏览/下载：274/0  |  提交时间：2020/06/22
Heart beat  Electrocardiography  Heart rate variability  Deep learning  Feature extraction  Task analysis  Informatics  ECG classification  biomedical monitoring  convolutional neural network  deep learning