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First-in-human liver-tumour surgery guided by multispectral fluorescence imaging in the visible and near-infrared-I/II windows 期刊论文
NATURE BIOMEDICAL ENGINEERING, 2019, 卷号: 4, 期号: 3, 页码: 16
作者:  Hu, Zhenhua;  Fang, Cheng;  Li, Bo;  Zhang, Zeyu;  Cao, Caiguang;  Cai, Meishan;  Su, Song;  Sun, Xingwang;  Shi, Xiaojing;  Li, Cong;  Zhou, Tiejun;  Zhang, Yuanxue;  Chi, Chongwei;  He, Pan;  Xia, Xianming;  Chen, Yue;  Gambhir, Sanjiv Sam;  Cheng, Zhen;  Tian, Jie
Adobe PDF(2918Kb)  |  收藏  |  浏览/下载：455/88  |  提交时间：2020/03/30
A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer 期刊论文
JOURNAL OF BIOPHOTONICS, 2019, 页码: 11
作者:  Zhang, Zeyu;  Qu, Yawei;  Cao, Yu;  Shi, Xiaojing;  Guo, Hongbo;  Zhang, Xiaojun;  Zheng, Sheng;  Liu, Haifeng;  Hu, Zhenhua;  Tian, Jie
收藏  |  浏览/下载：282/0  |  提交时间：2020/03/30
Cerenkov luminescence imaging  colorectal cancer  image-guided surgery  in vivo  
Adversarial image generation by combining content and style 期刊论文
IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 14, 页码: 2716-2723
作者:  Liu, Songyan;  Zhao, Chaoyang;  Gao, Yunze;  Wang, Jinqiao;  Tang, Ming
Adobe PDF(4319Kb)  |  收藏  |  浏览/下载：393/112  |  提交时间：2020/03/30
image recognition  feature extraction  learning (artificial intelligence)  image texture  adversarial image generation  unique style  reference images  style feature extraction module  style specific image generation model  double-cycle training strategy  natural-content pairs  input natural images  style exchange  style-exchanged images  licence-plate image  handbags images  
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments 会议论文
, Sydney, Australia, 2019.12.12-2019.12.15
作者:  Zhen-Liang Ni;  Gui-Bin Bian;  Xiao-Hu Zhou;  Zeng-Guang Hou;  Xiao-Liang Xie;  Chen Wang;  Yan-Jie Zhou;  Rui-Qi Li;  Zhen Li
Adobe PDF(2316Kb)  |  收藏  |  浏览/下载：216/38  |  提交时间：2022/06/15
surgical instrument segmentation  Robotics and Vision  
基于深度卷积网络的有监督图像特征提取和分类研究 学位论文
工学博士, 中国科学院自动化研究所: 中国科学院自动化研究所, 2019
作者:  Zhang Jinpeng
Adobe PDF(10324Kb)  |  收藏  |  浏览/下载：438/13  |  提交时间：2019/12/31
特征提取，图像分类，目标检测，卷积神经网络  
Towards Real-Time Advancement of Underwater Visual Quality With GAN 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 卷号: 66, 期号: 12, 页码: 9350-9359
作者:  Chen, Xingyu;  Yu, Junzhi;  Kong, Shihan;  Wu, Zhengxing;  Fang, Xi;  Wen, Li
浏览  |  Adobe PDF(4984Kb)  |  收藏  |  浏览/下载：414/138  |  提交时间：2019/12/16
Generative adversarial networks (GAN) image restoration  machine learning  underwater vision  
A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network 期刊论文
Physics in Medicine & Biology, 2019, 卷号: 64, 期号: 2019, 页码: 245010
作者:  Zhang,Zeyu;  Cai,Meishan;  Gao,Yuan;  Shi,Xiaojing;  Zhang,Xiaojun;  Hu,Zhenhua;  Tian,Jie
Adobe PDF(3210Kb)  |  收藏  |  浏览/下载：365/71  |  提交时间：2020/03/30
Cerenkov luminescence tomography (CLT)  optical reconstruction  photon propagation  neural network  inverse problem  
Noninvasive Imaging for Assessment of the Efficacy of Therapeutic Agents for Hepatocellular Carcinoma 期刊论文
Molecular Imaging and Biology, 2019, 期号: 10.1007/s11307-019-01431-5, 页码: 1-14
作者:  Liang, Qian;  Kong, Lingxin;  Zhu, Xu;  Du, Yang;  Tian, Jie
浏览  |  Adobe PDF(3951Kb)  |  收藏  |  浏览/下载：310/109  |  提交时间：2020/06/03
Hepatocellular carcinoma  Molecular imaging  Preclinical drug evaluation  Clinical trial  Therapeutics  
Disentangled Representation Learning of Makeup Portraits in the Wild 期刊论文
International Journal of Computer Vision, 2019, 卷号: -, 期号: -, 页码: -
作者:  Li, Yi;  Huang, Huaibo;  Cao, Jie;  He, Ran;  Tan, Tieniu
浏览  |  Adobe PDF(24456Kb)  |  收藏  |  浏览/下载：282/64  |  提交时间：2020/06/10
Face verification  Makeup transfer  Disentangled feature  Correspondence field  
GaitNet: An end-to-end network for gait based human identification 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 96, 期号: 106988, 页码: 11
作者:  Song, Chunfeng;  Huang, Yongzhen;  Huang, Yan;  Jia, Ning;  Wang, Liang
Adobe PDF(3015Kb)  |  收藏  |  浏览/下载：572/155  |  提交时间：2019/12/16
Gait recognition  Video-based human identification  End-to-end CNN  Joint learning