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Brainnetome atlas of preadolescent children based on anatomical connectivity profiles 期刊论文
CEREBRAL CORTEX, 2022, 页码: 12
作者:  Li, Wen;  Fan, Lingzhong;  Shi, Weiyang;  Lu, Yuheng;  Li, Jin;  Luo, Na;  Wang, Haiyan;  Chu, Congying;  Ma, Liang;  Song, Ming;  Li, Kaixin;  Cheng, Luqi;  Cao, Long;  Jiang, Tianzi
Adobe PDF(1764Kb)  |  收藏  |  浏览/下载：339/65  |  提交时间：2022/11/14
anatomical connectivity  Brainnetome atlas  development  diffusion magnetic resonance imaging  preadolescent child  
Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement 期刊论文
CEREBRAL CORTEX, 2022, 页码: 18
作者:  Shi, Weiyang;  Fan, Lingzhong;  Wang, Haiyan;  Liu, Bing;  Li, Wen;  Li, Jin;  Cheng, Luqi;  Chu, Congying;  Song, Ming;  Sui, Jing;  Luo, Na;  Cui, Yue;  Dong, Zhenwei;  Lu, Yuheng;  Ma, Yawei;  Ma, Liang;  Li, Kaixin;  Chen, Jun;  Chen, Yunchun;  Guo, Hua;  Li, Peng;  Lu, Lin;  Lv, Luxian;  Wan, Ping;  Wang, Huaning;  Wang, Huiling;  Yan, Hao;  Yan, Jun;  Yang, Yongfeng;  Zhang, Hongxing;  Zhang, Dai;  Jiang, Tianzi
Adobe PDF(4129Kb)  |  收藏  |  浏览/下载：433/147  |  提交时间：2022/09/19
schizophrenia  subtype  structural MRI  neuroimaging-clinic association analysis  neuroimaging-transcription association analysis  
基于磁共振影像的脑龄预测与大脑发展老化模式研究 学位论文
, 中国科学院自动化研究所: 中国科学院自动化研究所, 2022
作者:  胡天宇
Adobe PDF(27463Kb)  |  收藏  |  浏览/下载：256/5  |  提交时间：2022/06/13
磁共振成像  脑龄预测  深度学习  大脑老化  个体差异  
Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score 期刊论文
FRONTIERS IN GENETICS, 2022, 卷号: 13, 页码: 11
作者:  Wang, Meng;  Hu, Ke;  Fan, Lingzhong;  Yan, Hao;  Li, Peng;  Jiang, Tianzi;  Liu, Bing
Adobe PDF(822Kb)  |  收藏  |  浏览/下载：337/107  |  提交时间：2022/06/06
schizophrenia  treatment prediction  XGBoost  polygenic risk score  magnetic resonance imaging  
基于调强放疗大数据的患者剂量验证方法建模研究 学位论文
工学博士, 中国科学院自动化所: 中国科学院大学, 2021
作者:  王乐
Adobe PDF(5726Kb)  |  收藏  |  浏览/下载：174/2  |  提交时间：2022/03/07
肿瘤放射治疗，机器学习，深度学习，预测分类，基于患者的剂量验证， 容积旋转调强治疗  
Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario 期刊论文
RADIOTHERAPY AND ONCOLOGY, 2021, 卷号: 161, 期号: 10.1016/j.radonc.2021.06.024, 页码: 230-240
作者:  Yang, Ruijie;  Yang, Xueying;  Wang, Le;  Li, Dingjie;  Guo, Yuexin;  Li, Ying;  Guan, Yumin;  Wu, Xiangyang;  Xu, Shouping;  Zhang, Shuming;  Chan, Maria F.;  Geng, Lisheng;  Sui, Jing
Adobe PDF(2840Kb)  |  收藏  |  浏览/下载：339/55  |  提交时间：2021/11/02
Machine learning  VMAT patient-specific QA  Multi-institution validation  Commissioning  Clinical implementation  
The effectiveness of emotion cognitive reappraisal as measured by self-reported response and its link to EEG alpha asymmetry 期刊论文
Behavioural Brain Research, 2021, 卷号: 400, 页码: 113042
作者:  Li, Wenjie;  Li, Yingjie;  Cao, Dan
Adobe PDF(2103Kb)  |  收藏  |  浏览/下载：119/74  |  提交时间：2023/04/25
Multi-task autoencoder based classification-regression model for patient-specific VMAT QA 期刊论文
PHYSICS IN MEDICINE AND BIOLOGY, 2020, 卷号: 65, 期号: 23, 页码: 12
作者:  Wang, Le;  Li, Jiaqi;  Zhang, Shuming;  Zhang, Xile;  Zhang, Qilin;  Chan, Maria F.;  Yang, Ruijie;  Sui, Jing
Adobe PDF(926Kb)  |  收藏  |  浏览/下载：397/82  |  提交时间：2021/01/06
VMAT QA  patient-specific QA  deep learning  radiotherapy  
Prognostic models for prolonged disorders of consciousness: an integrative review 期刊论文
CELLULAR AND MOLECULAR LIFE SCIENCES, 2020, 期号: 8, 页码: 17
作者:  Song, Ming;  Yang, Yi;  Yang, Zhengyi;  Cui, Yue;  Yu, Shan;  He, Jianghong;  Jiang, Tianzi
Adobe PDF(2724Kb)  |  收藏  |  浏览/下载：319/46  |  提交时间：2020/06/22
Disorders of consciousness  Prognostic model  Outcome  Conscious recovery  Prediction  
Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy 期刊论文
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2019, 卷号: 105, 期号: 4, 页码: 893-902
作者:  Li, Jiaqi;  Wang, Le;  Zhang, Xile;  Liu, Lu;  Li, Jun;  Chan, Maria F.;  Sui, Jing;  Yang, Ruijie
Adobe PDF(1690Kb)  |  收藏  |  浏览/下载：284/37  |  提交时间：2019/09/30
VMAT  Machine Learning  Patient-Specific Quality Assurance