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Artificial intelligence in gastric cancer: applications and challenges 期刊论文
GASTROENTEROLOGY REPORT, 2022, 卷号: 10, 页码: 16
作者:  Cao, Runnan;  Tang, Lei;  Fang, Mengjie;  Zhong, Lianzhen;  Wang, Siwen;  Gong, Lixin;  Li, Jiazheng;  Dong, Di;  Tian, Jie
收藏  |  浏览/下载：248/0  |  提交时间：2023/01/09
gastric cancer  artificial intelligence  radiomics  endoscopy  computed tomography  pathology  
ASCL: Adversarial supervised contrastive learning for defense against word substitution attacks 期刊论文
NEUROCOMPUTING, 2022, 卷号: 510, 页码: 59-68
作者:  Shi, Jiahui;  Li, Linjing;  Zeng, Daniel
Adobe PDF(1054Kb)  |  收藏  |  浏览/下载：260/31  |  提交时间：2022/11/14
Adversarial example  Adversarial training  Model robustness  Contrastive learning  Natural language processing  
Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes 期刊论文
BMC Bioinformatics, 2022, 卷号: 23, 期号: 1, 页码: 23
作者:  Hong,Bei;  Liu,Jing;  Zhai,Hao;  Liu,Jiazheng;  Shen,Lijun;  Chen,Xi;  Xie,Qiwei;  Han,Hua
Adobe PDF(3091Kb)  |  收藏  |  浏览/下载：361/82  |  提交时间：2022/11/14
Connectomics  Reconstruction  Connectivity concept  Joint optimization  Electron microscope volumes  
The potential of prostate gland radiomic features in identifying the Gleason score 期刊论文
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 卷号: 144, 页码: 11
作者:  Gong, Lixin;  Xu, Min;  Fang, Mengjie;  He, Bingxi;  Li, Hailin;  Fang, Xiangming;  Dong, Di;  Tian, Jie
收藏  |  浏览/下载：293/0  |  提交时间：2022/07/25
MRI  Radiomics  Prostate cancer  Gleason score  Gland segmentation  
Entity Matters in News: An Association Network-Enhanced Method for News Reprint Prediction 期刊论文
IEEE INTELLIGENT SYSTEMS, 2022, 卷号: 37, 期号: 1, 页码: 99-107
作者:  Li, Qiudan;  Liu, Hejing;  Yao, Riheng;  Xu, David Jingjun;  Zeng, Daniel D.
收藏  |  浏览/下载：219/0  |  提交时间：2022/07/25
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
Adobe PDF(1293Kb)  |  收藏  |  浏览/下载：265/8  |  提交时间：2022/07/25
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：358/44  |  提交时间：2022/06/10
Tumor biomarkers  immunotherapy  lung neoplasms  programmed cell death 1 receptor (PD-1 receptor)  biostatistics  
Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma 期刊论文
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 页码: 11
作者:  Zhao, Xun;  Liang, Yu-Jing;  Zhang, Xu;  Wen, Dong-Xiang;  Fan, Wei;  Tang, Lin-Quan;  Dong, Di;  Tian, Jie;  Mai, Hai-Qiang
Adobe PDF(1720Kb)  |  收藏  |  浏览/下载：347/49  |  提交时间：2022/06/10
Recurrent nasopharyngeal carcinoma  Survival analysis  Radiomics  Deep learning  
FedFV: federated face verification via equivalent class embeddings 期刊论文
MULTIMEDIA SYSTEMS, 2022, 页码: 11
作者:  Liu, Lingyun;  Zhang, Yifan;  Gao, Haoyuan;  Yu, Xingtao;  Cheng, Jian
Adobe PDF(1435Kb)  |  收藏  |  浏览/下载：382/71  |  提交时间：2022/06/10
Equivalent class embeddings  Federated learning  Face recognition  Deep learning  
Attention Enhanced Reinforcement Learning for Multi agent Cooperation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 15
作者:  Pu, Zhiqiang;  Wang, Huimu;  Liu, Zhen;  Yi, Jianqiang;  Wu, Shiguang
Adobe PDF(2967Kb)  |  收藏  |  浏览/下载：344/51  |  提交时间：2022/06/06
Training  Reinforcement learning  Games  Scalability  Task analysis  Standards  Optimization  Attention mechanism  deep reinforcement learning (DRL)  graph convolutional networks  multi agent systems