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Wenhui Zhang

Government of the People's Republic of China - Department of Urology

SCHOLARLY PAPERS

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Scholarly Papers (2)

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1.

Multi-Modal Deep Learning Model for Predicting Homologous Recombination Deficiency in Prostate Cancer: An International Multi-Cohort Study

Number of pages: 35 Posted: 03 Aug 2023
Zijian Song, Qianwen Zhang, Wenhui Zhang, Na Ta, Yan Zhu, Longxin Deng, Han Wu, Lingxuan Zhu, Yaoming Li, Xiao Wang, Yang Yang, Wei Zhang, Yancheng Lai, Yan Wang, Min Qu, Zhengyang Dong, Huamao Ye, Linhu Wang, Gao Xu and Rui Chen
Government of the People's Republic of China - Department of Urology, Naval Medical University - Changhai Hospital, Government of the People's Republic of China - Department of Urology, Naval Medical University - Changhai Hospital, Naval Medical University - Changhai Hospital, Naval Medical University - Changhai Hospital, Naval Medical University - Changhai Hospital, Southern Medical University - Department of Oncology, Government of the People's Republic of China - Army Medical University, Zhejiang University - Department of Urology, Nanjing University - Department of Clinical Laboratory Medicine, Government of the People's Republic of China - Department of Urology, Government of the People's Republic of China - Department of Urology, Naval Medical University (SMMU), Naval Medical University (SMMU), Naval Medical University (SMMU), Naval Medical University (SMMU), Naval Medical University - Changhai Hospital, Naval Medical University - Changhai Hospital and Government of the People's Republic of China - Department of Urology
Downloads 182 (415,884)

Abstract:

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prostate cancer, homologous recombination repair, deep learning, molecular subtype, histopathology, magnetic resonance imaging

2.

Artificial Intelligence-Aided Detection for Prostate Cancer with Multi-Modal Routine Health Check-Up Data: An Asian Multi-Center Study

Number of pages: 39 Posted: 26 Jul 2023
Zijian Song, Wei Zhang, Qingchao Jiang, Le Du, Longxin Deng, Weiming Mou, Yancheng Lai, Wenhui Zhang, Yang Yang, Jasmine Lim, Kang Liu, Jae Young Park, Chi-Fai Ng, Ong Teng Aik, Qiang Wei, Lei Li, Xuedong Wei, Ming Chen, Zhixing Cao, Fubo Wang and Rui Chen
Government of the People's Republic of China - Department of Urology, Government of the People's Republic of China - Department of Urology, East China University of Science and Technology (ECUST) - Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology (ECUST) - Key Laboratory of Smart Manufacturing in Energy Chemical Process, Government of the People's Republic of China - Department of Urology, Government of the People's Republic of China - Department of Urology, Government of the People's Republic of China - Department of Urology, Government of the People's Republic of China - Department of Urology, Nanjing University - Department of Clinical Laboratory Medicine, University of Malaya, The Chinese University of Hong Kong (CUHK), Korea University - Korea University Ansan Hospital, The Chinese University of Hong Kong (CUHK), University of Malaya (UM), Sichuan University - Department of Urology, Xi'an Jiaotong University (XJTU) - Department of Urology, Soochow University - First Affiliated Hospital, Southeast University - Department of Urology, East China University of Science and Technology (ECUST) - Key Laboratory of Smart Manufacturing in Energy Chemical Process, Guangxi Medical University and Government of the People's Republic of China - Department of Urology
Downloads 97 (704,534)
Citation 1

Abstract:

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Prostate cancer, diagnosis, prostate biopsy, artificial intelligence, risk prediction