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：
Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography 期刊论文
IEEE Transactions on Medical Imaging, 2019, 卷号: 38, 期号: 12, 页码: 2726-2734
作者:  Hui Meng;  Kun Wang;  Yuan Gao;  Yushen Jin;  Xibo Ma;  Jie Tian
浏览  |  Adobe PDF(2085Kb)  |  收藏  |  浏览/下载：333/85  |  提交时间：2019/09/26
Fluorescence Tomography  Multi-modality Fusion  Brain  
Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images 期刊论文
EUROPEAN RADIOLOGY, 2019, 卷号: 29, 期号: 3, 页码: 1625-1634
作者:  Niu, Jianxing;  Zhang, Shuaitong;  Ma, Shunchang;  Diao, Jinfu;  Zhou, Wenjianlong;  Tian, Jie;  Zang, Yali;  Jia, Wang
浏览  |  Adobe PDF(1261Kb)  |  收藏  |  浏览/下载：420/92  |  提交时间：2019/07/12
Pituitary adenomas  Cavernous sinus  Neoplasm invasion  Nomogram  Support vector machine  
Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer 期刊论文
ANNALS OF ONCOLOGY, 2019, 卷号: 30, 期号: 3, 页码: 431-438
作者:  Dong, Di;  Tang, Lei;  Li, Ziyu;  Fang, Mengjie;  Gao, Jianbo;  Shan, Xiuhong;  Ying, Xiangji;  Sun, Yingshi;  Fu, Jia;  Wang, Xiaoxiao;  Li, Liming;  Li, Zhenhui;  Zhang, Dafu;  Zhang, Yan;  Li, Zhemin;  Shan, Fei;  Bu, Zhaode;  Tian, Jie;  Ji, Jiafu
浏览  |  Adobe PDF(593Kb)  |  收藏  |  浏览/下载：504/87  |  提交时间：2019/07/12
occult peritoneal metastasis  radiomic nomogram  advanced gastric cancer  
Reconstruction of Fluorescence Molecular Tomography via a Fused LASSO Method Based on Group Sparsity Prior 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2019, 卷号: 66, 期号: 5, 页码: 1361-1371
作者:  Jiang, Shixin;  Liu, Jie;  Zhang, Guanglei;  An, Yu;  Meng, Hui;  Gao, Yuan;  Wang, Kun;  Tian, Jie
浏览  |  Adobe PDF(5641Kb)  |  收藏  |  浏览/下载：568/134  |  提交时间：2019/07/11
Fluorescence molecular tomography  image reconstruction  fused LASSO method  group sparsity