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Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study 期刊论文
GASTRIC CANCER, 2023, 页码: 12
作者:  Fang, Shuangshuang;  Liu, Zhenyu;  Qiu, Qi;  Tang, Zhenchao;  Yang, Yang;  Kuang, Zhongsheng;  Du, Xiaohua;  Xiao, Shanshan;  Liu, Yanyan;  Luo, Yuanbin;  Gu, Liping;  Tian, Li;  Liang, Xiaoxia;  Fan, Guiling;  Zhang, Yu;  Zhang, Ping;  Zhou, Weixun;  Liu, Xiuli;  Tian, Jie;  Wei, Wei
收藏  |  浏览/下载：47/0  |  提交时间：2024/02/22
Atrophic gastritis  Semi-supervised deep learning  Diagnose  The operative link for gastric intestinal metaplasia assessment  The operative link for gastritis assessment  
Patient-level grading prediction of prostate cancer from mp-MRI via GMINet 期刊论文
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 卷号: 150, 页码: 10
作者:  Shao, Lizhi;  Liu, Zhenyu;  Liu, Jiangang;  Yan, Ye;  Sun, Kai;  Liu, Xiangyu;  Lu, Jian;  Tian, Jie
收藏  |  浏览/下载：169/0  |  提交时间：2022/11/21
mp-MRI  Prostate cancer  Grade group  Patient-level prediction  Deep learning  
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  
基于定量磁共振影像组学的脑肿瘤无创诊断研究 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2020
作者:  张帅通
Adobe PDF(5591Kb)  |  收藏  |  浏览/下载：254/5  |  提交时间：2020/06/12
磁共振影像（MRI）  影像组学  无创诊断  半监督学习  脑肿瘤  
乳腺癌近红外激发荧光成像方法研究和临床应用探索 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2020
作者:  张崇
Adobe PDF(5418Kb)  |  收藏  |  浏览/下载：315/3  |  提交时间：2020/06/03
近红外激发荧光成像  手术导航  图像处理  乳腺癌  
Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics 期刊论文
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2020, 页码: 8
作者:  Gong, Lixin;  Xu, Min;  Fang, Mengjie;  Zou, Jian;  Yang, Shudong;  Yu, Xinyi;  Xu, Dandan;  Zhou, Lijuan;  Li, Hailin;  He, Bingxi;  Wang, Yan;  Fang, Xiangming;  Dong, Di;  Tian, Jie
Adobe PDF(733Kb)  |  收藏  |  浏览/下载：287/39  |  提交时间：2020/06/02
prostate cancer  radiomics  Gleason score  biparametric MRI  
Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy 期刊论文
THERANOSTICS, 2020, 卷号: 10, 期号: 22, 页码: 10200-10212
作者:  Shao, Lizhi;  Yan, Ye;  Liu, Zhenyu;  Ye, Xiongjun;  Xia, Haizhui;  Zhu, Xuehua;  Zhang, Yuting;  Zhang, Zhiying;  Chen, Huiying;  He, Wei;  Liu, Cheng;  Lu, Min;  Huang, Yi;  Ma, Lulin;  Sun, Kai;  Zhou, Xuezhi;  Yang, Guanyu;  Lu, Jian;  Tian, Jie
收藏  |  浏览/下载：236/0  |  提交时间：2021/03/02
prostate cancer  Gleason grade groups  deep reinforcement learning  prostate cancer grading  magnetic resonance imaging  
A deep learning radiomics model for preoperative grading in meningioma 期刊论文
EUROPEAN JOURNAL OF RADIOLOGY, 2019, 卷号: 116, 页码: 128-134
作者:  Zhu, Yongbei;  Man, Chuntao;  Gong, Lixin;  Dong, Di;  Yu, Xinyi;  Wang, Shuo;  Fang, Mengjie;  Wang, Siwen;  Fang, Xiangming;  Chen, Xuzhu;  Tian, Jie
Adobe PDF(1336Kb)  |  收藏  |  浏览/下载：367/30  |  提交时间：2019/07/11
Radiomics  Deep learning  Meningioma  Tumor grading  Magnetic resonance imaging  
CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study 期刊论文
EUROPEAN RADIOLOGY, 2019, 卷号: 29, 期号: 12, 页码: 6880-6890
作者:  Gu, Dongsheng;  Hu, Yabin;  Ding, Hui;  Wei, Jingwei;  Chen, Ke;  Liu, Hao;  Zeng, Mengsu;  Tian, Jie
Adobe PDF(1291Kb)  |  收藏  |  浏览/下载：348/62  |  提交时间：2020/03/30
Neoplasm grading  Pancreas  Neuroendocrine tumor  Radiomics  CT  
Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method 期刊论文
EUROPEAN JOURNAL OF RADIOLOGY, 2019, 卷号: 115, 页码: 16-21
作者:  Min, Xiangde;  Li, Min;  Dong, Di;  Feng, Zhaoyan;  Zhang, Peipei;  Ke, Zan;  You, Huijuan;  Han, Fangfang;  Ma, He;  Tian, Jie;  Wang, Liang
收藏  |  浏览/下载：272/0  |  提交时间：2019/07/12
Magnetic resonance imaging  Prostatic neoplasms  Neoplasm grading  Radiomics