Dr. Zhao Yuanshen is a prominent figure in the field of medical artificial intelligence, affiliated with the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. With a background in Mechanical and Electronic Engineering, Dr. Zhao has made significant contributions to the field through his expertise in intelligent analysis of tumor multimodal data, including imaging-genomics, machine learning, and deep learning techniques. He has garnered recognition for his work and is a recipient of various awards and honors, including the Shenzhen High-Level Talent award.
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Dr. Zhao Yuanshen’s contributions to academic literature are notable, with a substantial number of publications in reputable journals and conferences. His work has garnered citations and recognition within the scientific community, indicating the impact of his research in the field of medical artificial intelligence.
- Citations: Dr. Zhao’s work has received a total of 576 citations from 506 documents. This indicates the impact and reach of Dr. Zhao’s research within the academic community.
- Documents: Dr. Zhao has authored 8 documents that are indexed in the Scopus database.
- h-index: Dr. Zhao’s h-index, a metric often used to measure the productivity and impact of an author’s publications, is 6.
Education
Dr. Zhao’s academic journey began with a Bachelor’s degree in Mechanical and Electronic Engineering from North China University. He pursued further studies and obtained a Master’s degree from the University of Shanghai for Science and Technology before completing his Ph.D. in Mechanical and Electronic Engineering from Shanghai Jiao Tong University.
Research Focus
Dr. Zhao specializes in the intelligent analysis of tumor multimodal data, with a particular focus on imaging-genomics, machine learning, and deep learning techniques. His research aims to advance medical artificial intelligence applications, particularly in the diagnosis, prognosis, and treatment of various types of cancer.
Professional Journey
Dr. Zhao’s professional journey includes roles as a Postdoctoral Fellow and Assistant Researcher at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He has demonstrated leadership in various research projects and has contributed significantly to advancing the field of medical artificial intelligence.
Honors & Awards
Throughout his career, Dr. Zhao has received recognition for his contributions to the field. He was awarded the Shenzhen High-Level Talent (Reserve Level) in 2021 and was the recipient of the Advanced Youth Innovation Fund in 2023, highlighting his achievements and dedication to research excellence.
Publications Noted & Contributions
Dr. Zhao has authored numerous publications in esteemed journals and conferences, focusing on topics such as tumor multimodal data analysis, imaging-genomics, machine learning, and deep learning. His contributions have advanced the understanding and application of medical artificial intelligence in the diagnosis and treatment of cancer.
“A hybrid classification model with radiomics and CNN for high and low grading of prostate cancer Gleason score on mp-MRI”
Authors: F. Liu, Y. Zhao, J. Song, G. Tu, Y. Liu, Y. Peng, J. Mao, C. Yan, R. Wang
Published in: Displays, 2024
Summary: This paper presents a hybrid classification model combining radiomics and Convolutional Neural Network (CNN) for grading prostate cancer Gleason scores on multiparametric MRI (mp-MRI), distinguishing between high and low grades.
“MRI and RNA-seq fusion for prediction of pathological response to neoadjuvant chemotherapy in breast cancer”
Authors: H. Li, Y. Zhao, J. Duan, J. Gu, Z. Liu, H. Zhang, Y. Zhang, Z.C. Li
Published in: Displays, 2024
Summary: This study proposes a fusion of MRI and RNA sequencing (RNA-seq) data to predict the pathological response to neoadjuvant chemotherapy in breast cancer patients.
“Identifying pathological groups from MRI in prostate cancer using graph representation learning”
Authors: F. Liu, Y. Zhao, C. Yan, J. Duan, L. Tang, B. Gao, R. Wang
Published in: Displays, 2024
Summary: This research focuses on identifying pathological groups in prostate cancer using graph representation learning applied to MRI data.
“Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole-Slide Images in Adult Patients with Diffuse Glioma”
Authors: Y. Zhao, W. Wang, Y. Ji, Y. Guo, J. Duan, X. Liu, D. Yan, D. Liang, W. Li, …
Published in: The American Journal of Pathology, 2024
Summary: This paper explores computational pathology methods to predict Isocitrate Dehydrogenase (IDH) gene mutation from whole-slide images in adult patients with diffuse glioma, contributing to precision medicine approaches.
“Imaging-proteomics co-profiling reveals biologic pathways underlying prognostic MRI features”
Authors: J. Duan, Y. Zhao, Z. Zhang, D. Liang, Z.C. Li, Z. Liu, X. Chen
Presented at: 15th Biomedical Engineering International Conference (BMEiCON), 2023
Summary: This conference paper discusses the use of imaging-proteomics co-profiling to uncover biological pathways associated with prognostic MRI features, providing insights into disease mechanisms and potential therapeutic targets.
Research Timeline
Dr. Zhao’s research timeline reflects his commitment to advancing the field of medical artificial intelligence. He has been involved in various research projects, including hosting and participating in projects funded by prestigious organizations such as the National Natural Science Foundation of China and the Guangdong Provincial Natural Science Foundation.
Collaborations and Projects
Dr. Zhao has collaborated with researchers and institutions both nationally and internationally on projects related to medical artificial intelligence. His collaborations have led to innovative approaches and advancements in the field, contributing to the development of novel solutions for cancer diagnosis, prognosis, and treatment.
