中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共5条，第1-5条 帮助

已选(0)清除 条数/页：   排序方式：
Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study 期刊论文
ULTRASCHALL IN DER MEDIZIN, 2023, 页码: 11
作者:  Zhu, Yangyang;  Meng, Zheling;  Wu, Hao;  Fan, Xiao;  Lv, Wenhao;  Tian, Jie;  Wang, Kun;  Nie, Fang
收藏  |  浏览/下载：31/0  |  提交时间：2024/02/22
Multimodal Ultrasound  Metastatic Cervical Lymphadenopathy  Deep Learning  Primary Cancer Sites  
CEUSegNet: A Cross-Modality Lesion Segmentation Network for Contrast-Enhanced Ultrasound 会议论文
, Kolkata, India, 2022.3.28-31
作者:  Zheling MENG;  Yangyang ZHU;  Xiao FAN;  Jie TIAN;  Fang NIE;  Kun WANG
Adobe PDF(1363Kb)  |  收藏  |  浏览/下载：90/19  |  提交时间：2023/05/11
MSMFN: An ultrasound based multi-step modality fusion network for identifying the histologic subtypes of metastatic cervical lymphadenopathy 期刊论文
IEEE Transactions on Medical Imaging, 2022, 页码: 1-13
作者:  Zheling, Meng;  Yangyang, Zhu;  Wenjing, Pang;  Jie, Tian;  Fang, Nie;  Kun, Wang
Adobe PDF(3049Kb)  |  收藏  |  浏览/下载：243/48  |  提交时间：2023/03/27
Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy 期刊论文
BMC Medicine, 2022, 卷号: 20, 期号: 1, 页码: 1-13
作者:  Zhu,Yangyang;  Meng,Zheling;  Fan,Xiao;  Duan,Yin;  Jia,Yingying;  Dong,Tiantian;  Wang,Yanfang;  Song,Juan;  Tian,Jie;  Wang,Kun;  Nie,Fang
Adobe PDF(3297Kb)  |  收藏  |  浏览/下载：214/33  |  提交时间：2022/09/19
Deep learning  Cervical lymphadenopathy  Ultrasound  Reactive hyperplasia  Tuberculous lymphadenitis  Lymphoma  Metastatic carcinoma  
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：479/173  |  提交时间：2019/05/05
Brain Image Labeling, Multi-atlas-based Method, Fully Convolutional Network, Patch-based Labeling