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Doubly Semi-Supervised Multimodal Adversarial Learning for Classification, Generation and Retrieval 会议论文
, 上海, 2019/7/8
作者:  Du CD(杜长德);  Du CY(杜长营);  He HG（何晖光）
Adobe PDF(636Kb)  |  收藏  |  浏览/下载：100/34  |  提交时间：2023/05/05
Learning "What" and "Where": An Interpretable Neural Encoding Model 会议论文
, Budapest, Hungary, July 14-19, 2019
作者:  Wang, Haibao;  Huang, Lijie;  Du, Changde;  He, Huiguang
浏览  |  Adobe PDF(865Kb)  |  收藏  |  浏览/下载：250/47  |  提交时间：2020/06/10
Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 8, 页码: 2310-2323
作者:  Du, Changde;  Du, Changying;  Huang, Lijie;  He, Huiguang
Adobe PDF(3773Kb)  |  收藏  |  浏览/下载：353/44  |  提交时间：2019/12/16
Deep neural network (DNN)  image reconstruction  multiview learning  neural decoding  variational Bayesian inference  
3D Shape Reconstruction of Lumbar Vertebra from Two X-ray Images and a CT Model 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2019, 卷号: 7, 期号: 10, 页码: 1-10
作者:  Fang, Longwei;  Wang, Zuowei;  Chen, Zhiqiang;  Jian, Fengzeng;  Li, Shuo;  He, Huiguang
浏览  |  Adobe PDF(5609Kb)  |  收藏  |  浏览/下载：432/120  |  提交时间：2019/07/01
3d Reconstruction  Vertebra Model  X-ray Image  2d/3d Registration  2d/2d Registration  
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models 期刊论文
Engineering, 2019, 期号: 0, 页码: 1-8
作者:  Du Changde;  Li Jinpeng;  Huang Lijie;  He Huiguang
浏览  |  Adobe PDF(497Kb)  |  收藏  |  浏览/下载：529/176  |  提交时间：2019/05/06
Brain Encoding And Decoding  Fmri  Deep Neural Networks  Deep Generative Models  Dual Learning  
Automatic brain labeling via multi-atlas guided fully convolutional networks 期刊论文
Medical Image Analysis, 2019, 期号: 52, 页码: 157-168
作者:  Longwei Fang;  Lichi Zhang;  Dong Nie;  Xiaohuan Cao;  Islem Rekik;  Seong-Whan Lee;  Huiguang He;  Dingguang Shen
浏览  |  Adobe PDF(2952Kb)  |  收藏  |  浏览/下载：522/180  |  提交时间：2019/05/05
Brain Image Labeling, Multi-atlas-based Method, Fully Convolutional Network, Patch-based Labeling