中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共34条，第1-10条 帮助

限定条件                
已选(0)清除 条数/页：   排序方式：
Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging 期刊论文
BIOLOGY-BASEL, 2024, 卷号: 13, 期号: 1, 页码: 17
作者:  Shang, Yaxin;  Liu, Jie;  Wang, Yueqi;  Bertrand, Helene
收藏  |  浏览/下载：9/0  |  提交时间：2024/03/26
magnetic particle imaging  convolutional neural network  transformer  tumor imaging  accurate quantification  
A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space 期刊论文
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2022, 卷号: 41, 期号: 3, 页码: 657-666
作者:  An, Yu;  Bian, Chang;  Yan, Daxiang;  Wang, Hanfan;  Wang, Yu;  Du, Yang;  Tian, Jie
Adobe PDF(8285Kb)  |  收藏  |  浏览/下载：276/57  |  提交时间：2022/06/06
Imaging  Image reconstruction  Mice  In vivo  Image segmentation  Finite element analysis  Surface reconstruction  Fluorescence molecular tomography  imaging reconstruction  standardized imaging space  
Cross-Phase Adversarial Domain Adaptation for Deep Disease-free Survival Prediction with Gastric Cancer CT Images 会议论文
, Mexico, Oct 31 - Nov 4, 2021
作者:  Wang Siwen;  Dong Di;  Li Hailin;  Feng Caizhen;  Wang Yi;  Tian Jie
Adobe PDF(1071Kb)  |  收藏  |  浏览/下载：176/38  |  提交时间：2022/06/14
PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Network 会议论文
, Montreal, BC, Canada, 11-17 October 2021
作者:  Shen, Biluo;  Xiao, Anqi;  Tian, Jie;  Hu, Zhenhua
Adobe PDF(235Kb)  |  收藏  |  浏览/下载：180/33  |  提交时间：2022/06/15
基于影像组学的肝细胞癌预后因子预测方法研究 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2021
作者:  顾东升
Adobe PDF(19880Kb)  |  收藏  |  浏览/下载：237/4  |  提交时间：2021/06/16
影像组学，肝细胞癌，预后因子，无创诊断，深度学习  
Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks 期刊论文
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2021, 卷号: 48, 期号: 11, 页码: 3482-3492
作者:  Shen, Biluo;  Zhang, Zhe;  Shi, Xiaojing;  Cao, Caiguang;  Zhang, Zeyu;  Hu, Zhenhua;  Ji, Nan;  Tian, Jie
Adobe PDF(1209Kb)  |  收藏  |  浏览/下载：333/60  |  提交时间：2021/05/17
Fluorescence imaging  Deep learning  Convolutional neural networks  Intraoperative pathology  Gliomas  
A review of the application of machine learning in molecular imaging 期刊论文
Annals of Translational Medicine, 2021, 卷号: 0, 期号: 0, 页码: 0
作者:  Yin, Lin;  Cao, Zhen;  Wang, Kun;  Tian, Jie;  Yang, Xing;  Zhang, Jianhua
Adobe PDF(4435Kb)  |  收藏  |  浏览/下载：173/34  |  提交时间：2021/06/16
molecular imaging, machine learning, artificial intelligence  
Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients 期刊论文
LIVER CANCER, 2020, 卷号: 9, 期号: 4, 页码: 397-413
作者:  Liu, Fei;  Liu, Dan;  Wang, Kun;  Xie, Xiaohua;  Su, Liya;  Kuang, Ming;  Huang, Guangliang;  Peng, Baogang;  Wang, Yuqi;  Lin, Manxia;  Tian, Jie;  Xie, Xiaoyan
浏览  |  Adobe PDF(1066Kb)  |  收藏  |  浏览/下载：325/80  |  提交时间：2020/09/07
Contrast-enhanced ultrasound  Hepatocellular carcinoma  Radiomics  Radiofrequency ablation  Surgical resection  
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study 期刊论文
ANNALS OF ONCOLOGY, 2020, 卷号: 31, 期号: 7, 页码: 912-920
作者:  Dong, Di;  Fang, Mengjie;  Tang, Lei;  Shan, Xiuhong;  Gao, Jianbo;  Giganti, Francesco;  Wang, Rongpin;  Chen, Xin;  Wang, Xiaoxiao;  Palumbo, Diego;  Fu, Jia;  Li, Wuchao;  Li, Jing;  Zhong, Lianzhen;  De Cobelli, Francesco;  Ji, Jiafu;  Liu, Zaiyi;  Tian, Jie
Adobe PDF(2209Kb)  |  收藏  |  浏览/下载：367/62  |  提交时间：2020/07/20
deep learning  locally advanced gastric cancer  lymph node metastasis  radiomic nomogram  
Deep learning -based multi -view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study 期刊论文
EUROPEAN JOURNAL OF RADIOLOGY, 2020, 卷号: 128, 期号: 109041, 页码: 9
作者:  Wu, Xiangjun;  Hui, Hui;  Niu, Meng;  Li, Liang;  Wang, Li;  He, Bingxi;  Yang, Xin;  Li, Li;  Li, Hongjun;  Tian, Jie;  Zha, Yunfei
浏览  |  Adobe PDF(2315Kb)  |  收藏  |  浏览/下载：273/43  |  提交时间：2020/07/20
Coronavirus disease 2019  Deep learning  Multi-view model  Computed tomography