CASIA OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共62条,第1-10条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
JOINT MULTI-TASK LEARNING FOR SURVIVAL PREDICTION OF GASTRIC CANCER PATIENTS USING CT IMAGES 会议论文
, 线上会议, 2021-4
作者:  Liwen Zhang;  Di Dong;  Zaiyi Liu;  Junlin Zhou;  Jie Tian
Adobe PDF(316Kb)  |  收藏  |  浏览/下载:5/0  |  提交时间:2024/06/21
Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer 期刊论文
MEDICAL PHYSICS, 2023, 页码: 11
作者:  Liu, Shengyuan;  Deng, Jingyu;  Dong, Di;  Fang, Mengjie;  Ye, Zhaoxiang;  Hu, Yanfeng;  Li, Hailin;  Zhong, Lianzhen;  Cao, Runnan;  Zhao, Xun;  Shang, Wenting;  Li, Guoxin;  Liang, Han;  Tian, Jie
Adobe PDF(2945Kb)  |  收藏  |  浏览/下载:164/1  |  提交时间:2023/11/17
deep learning  extranodal soft tissue metastasis  gastric cancer  radiomics  
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:217/50  |  提交时间:2022/06/14
Using multi-task learning to improve diagnostic performance of convolutional neural networks 会议论文
, San Diego, California, USA, 2019-2
作者:  Fang, Mengjie;  Dong, Di;  Sun, Ruijia;  Fan, Li;  Sun, Yingshi;  Liu, Shiyuan;  Tian, Jie
Adobe PDF(299Kb)  |  收藏  |  浏览/下载:130/36  |  提交时间:2022/06/14
Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning 会议论文
, San Diego, California, USA, 2019-2
作者:  Fang, Mengjie;  Zhang, Wenjuan;  Dong, Di;  Zhou, Junlin;  Tian, Jie
Adobe PDF(500Kb)  |  收藏  |  浏览/下载:169/64  |  提交时间:2022/06/14
Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk 期刊论文
TRANSLATIONAL LUNG CANCER RESEARCH, 2022, 页码: 23
作者:  He, Bing-Xi;  Zhong, Yi-Fan;  Zhu, Yong-Bei;  Deng, Jia-Jun;  Fang, Meng-Jie;  She, Yun-Lang;  Wang, Ting-Ting;  Yang, Yang;  Sun, Xi-Wen;  Belluomini, Lorenzo;  Watanabe, Satoshi;  Dong, Di;  Tian, Jie;  Xie, Dong
Adobe PDF(3742Kb)  |  收藏  |  浏览/下载:356/44  |  提交时间:2022/06/10
Tumor biomarkers  immunotherapy  lung neoplasms  programmed cell death 1 receptor (PD-1 receptor)  biostatistics  
Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer 期刊论文
MEDICAL PHYSICS, 2022, 页码: 12
作者:  Li, Cong;  Qin, Yun;  Zhang, Wei-Han;  Jiang, Hanyu;  Song, Bin;  Bashir, Mustafa R.;  Xu, Heng;  Duan, Ting;  Fang, Mengjie;  Zhong, Lianzhen;  Meng, Lingwei;  Dong, Di;  Hu, Zhenhua;  Tian, Jie;  Hu, Jian-Kun
Adobe PDF(1878Kb)  |  收藏  |  浏览/下载:426/95  |  提交时间:2022/03/17
chemotherapy  deep learning  diagnosis  signet-ring cell carcinoma  survival  
Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research 期刊论文
MEDICAL PHYSICS, 2021, 卷号: 48, 期号: 9, 页码: 5017-5028
作者:  Wang, Siwen;  Dong, Di;  Zhang, Wenjuan;  Hu, Hui;  Li, Hailin;  Zhu, Yongbei;  Zhou, Junlin;  Shan, Xiuhong;  Tian, Jie
Adobe PDF(2265Kb)  |  收藏  |  浏览/下载:279/42  |  提交时间:2021/11/02
advanced gastric cancer  Borrmann classification  ensemble learning  multilayer perceptron networks  radiomics  
Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer 期刊论文
BMC Medical Imaging, 2021, 卷号: 21, 期号: 1, 页码: 10
作者:  Wang,Xiaoxiao;  Li,Cong;  Fang,Mengjie;  Zhang,Liwen;  Zhong,Lianzhen;  Dong,Di;  Tian,Jie;  Shan,Xiuhong
Adobe PDF(2269Kb)  |  收藏  |  浏览/下载:426/71  |  提交时间:2021/04/06
Stomach cancer  Lymph nodes  Nomogram  
Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics 期刊论文
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 卷号: 24, 期号: 12, 页码: 3585-3594
作者:  Li, Cong;  Dong, Di;  Li, Liang;  Gong, Wei;  Li, Xiaohu;  Bai, Yan;  Wang, Meiyun;  Hu, Zhenhua;  Zha, Yunfei;  Tian, Jie
Adobe PDF(2325Kb)  |  收藏  |  浏览/下载:389/63  |  提交时间:2021/03/02
COVID-19  radiomics  deep learning  computed tomography (CT)