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Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images 期刊论文
JAMA NETWORK OPEN, 2022, 卷号: 5, 期号: 6, 页码: 11
作者:  Zhang, Liwen;  Dong, Di;  Sun, Yongqing;  Hu, Chaoen;  Sun, Congxin;  Wu, Qingqing;  Tian, Jie
收藏  |  浏览/下载：228/0  |  提交时间：2022/07/25
Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis 期刊论文
BMC MEDICINE, 2022, 卷号: 20, 期号: 1, 页码: 15
作者:  Tong, Tong;  Gu, Jionghui;  Xu, Dong;  Song, Ling;  Zhao, Qiyu;  Cheng, Fang;  Yuan, Zhiqiang;  Tian, Shuyuan;  Yang, Xin;  Tian, Jie;  Wang, Kun;  Jiang, Tian'an
Adobe PDF(9703Kb)  |  收藏  |  浏览/下载：355/58  |  提交时间：2022/06/06
Deep learning  Artificial intelligence  Pancreatic ductal adenocarcinoma  Contrast-enhanced ultrasound  Chronic pancreatitis  
Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2021, 卷号: 68, 期号: 12, 页码: 3690-3700
作者:  Shao, Lizhi;  Liu, Zhenyu;  Yan, Ye;  Liu, Jiangang;  Ye, Xiongjun;  Xia, Haizhui;  Zhu, Xuehua;  Zhang, Yuting;  Zhang, Zhiying;  Chen, Huiying;  He, Wei;  Liu, Cheng;  Lu, Min;  Huang, Yi;  Sun, Kai;  Zhou, Xuezhi;  Yang, Guanyu;  Lu, Jian;  Tian, Jie
收藏  |  浏览/下载：301/0  |  提交时间：2021/12/28
Magnetic resonance imaging  Principal component analysis  Pathology  Lesions  Optimization  Task analysis  Predictive models  Gleason score  prostate cancer  patient-level prediction  joint optimization  MRI  reinforcement learning  
Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study 期刊论文
MEDICAL PHYSICS, 2021, 页码: 12
作者:  Wu, Xiangjun;  Dong, Di;  Zhang, Lu;  Fang, Mengjie;  Zhu, Yongbei;  He, Bingxi;  Ye, Zhaoxiang;  Zhang, Minming;  Zhang, Shuixing;  Tian, Jie
Adobe PDF(3862Kb)  |  收藏  |  浏览/下载：677/358  |  提交时间：2021/05/06
deep learning  peritumor  radiomics