Dr. Zhenwei Shi, Guangdong Provincial People’s Hospital, China
🎓 Dr. Zhenwei Shi is a distinguished Postdoctoral Fellow in Clinical Medicine at Guangdong Provincial People’s Hospital, bringing a wealth of knowledge cultivated through a stellar academic journey. Having earned a Ph.D. in Clinical Data Science from Maastricht University and a Master’s in artificial intelligence from the University of Groningen, Netherlands, Dr. Shi seamlessly blends clinical medicine, data science, and AI expertise. In his dynamic professional trajectory, he serves as an Assistant Researcher at Southern Medical University/Guangdong Provincial People’s Hospital, contributing significantly to medical research. As the Research PI at the Guangdong Key Laboratory of Medical Image Analysis and Application, Dr. Shi exhibits a keen focus on advancing healthcare through innovative technology. Adorned with prestigious awards, including a Special Award for digital health innovation, Dr. Shi’s commitment to excellence is evident. His research interests in deep learning, quantitative imaging analysis, and oncology data integration, underscored by a passion for federated learning, position him as a visionary in the evolving landscape of healthcare technology. 🌐👨⚕️🔬
🎓 Education :
👨🎓 Dr. Zhenwei Shi has embarked on an illustrious educational journey, culminating in his current role as a Postdoctoral Fellow in Clinical Medicine at Guangdong Provincial People’s Hospital (2021-2023). His academic pursuits took him to Maastricht University in the Netherlands, where he earned a Ph.D. in Clinical Data Science from 2016 to 2020. Prior to that, Dr. Shi delved into the realm of artificial intelligence at the University of Groningen, the Netherlands, where he successfully obtained a Master’s degree from 2013 to 2016. With a rich background spanning clinical medicine, data science, and artificial intelligence, Dr. Shi brings a diverse skill set and a passion for advancing healthcare through innovative research and technology. 🌐📚🔬
🌐 Professional Profiles :
Google Scholar
Scopus
🔍 Experience :
👨🔬 Dr. Zhenwei Shi has seamlessly transitioned from his academic achievements to a dynamic professional trajectory. Currently serving as an Assistant Researcher at Southern Medical University/Guangdong Provincial People’s Hospital in Guangzhou, China, since 2023, Dr. Shi is actively contributing to the advancement of medical research. Simultaneously, he holds the position of Research PI at the Guangdong Key Laboratory of Medical Image Analysis and Application, based in Guangzhou, China, since 2020. In 2019, Dr. Shi broadened his expertise as a Visiting Scholar at the prestigious Dana-Farber Cancer Institute, affiliated with Harvard University in Boston, USA. With a diverse range of experiences, Dr. Zhenwei Shi continues to make impactful contributions to the fields of medical imaging, analysis, and application. 🌐💼
🏆Awards :
🏆 Dr. Zhenwei Shi stands adorned with accolades, showcasing his remarkable achievements in the realm of healthcare and digital innovation. His outstanding contributions were recognized with a Special Award at the First National Digital Health Innovation Application Competition, highlighting his prowess in leveraging technology for transformative healthcare solutions. Dr. Shi’s commitment to excellence is further underscored by his acknowledgment as a recipient of the High-level Talent Introduction at Guangdong Provincial People’s Hospital, reflecting his impact in the medical field.
Adding to his impressive list of honors, Dr. Shi has been selected as part of the Guangdong Provincial Overseas Postdoctoral Talent Support Program, affirming his status as a distinguished professional in his field. These awards not only acknowledge Dr. Zhenwei Shi’s dedication to advancing healthcare but also position him as a key figure in the integration of digital health innovations. 🌟💡👨⚕️
🧠 Research Interests 🔬🌐 :
🧠 Dr. Zhenwei Shi, with an insatiable curiosity and passion for innovation, delves into the forefront of cutting-edge research. His primary research interests span the expansive realms of deep learning, quantitative imaging analysis, and the integration of big data within the oncology domain. Dr. Shi is at the forefront of exploring the potential of federated learning, harnessing the power of decentralized data for collaborative advancements in healthcare. His expertise also extends to the intersection of deep learning and medicine, where he strives to unravel the transformative possibilities of artificial intelligence in shaping the future of medical practices. With an unwavering commitment to pushing the boundaries of knowledge, Dr. Zhenwei Shi stands as a visionary in the dynamic intersection of technology and healthcare. 🌐🔬🤖
📚 Publication Impact and Citations :
Scopus Metrics:
- 📝 Publications: 41 documents indexed in Scopus.
- 📊 Citations: A total of 423 citations for his publications, reflecting the widespread impact and recognition of Dr. Zhenwei Shi’s research within the academic community.
Google Scholar Metrics:
- All Time:
- Citations: 641 📖
- h-index: 15 📊
- i10-index: 20 🔍
- Since 2018:
- Citations: 638 📖
- h-index: 15 📊
- i10-index: 20 🔍
👨🏫 A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. 🌐🔬
Publications Top Notes :
1. Learning from scanners: Bias reduction and feature correction in radiomics
Published Year: 2019, Cited By: 65
Journal: Clinical and Translational Radiation Oncology
2. Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing
Published Year: 2019, Cited By: 60
Journal: Physica Medica
3. Distributed radiomics as a signature validation study using the Personal Health Train infrastructure
Published Year: 2019, Cited By: 53
Journal: Scientific Data
4. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study
Published Year: 2022, Cited By: 40
Journal: EClinicalMedicine
5. Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels
Published Year: 2022, Cited By: 39
Journal: Medical Image Analysis
6. Ontology‐guided radiomics analysis workflow (O‐RAW)
Published Year: 2019, Cited By: 39
Journal: Medical Physics
7. Multicenter CT phantoms public dataset for radiomics reproducibility tests
Published Year: 2019, Cited By: 35
Journal: Medical Physics
8. PDBL: Improving histopathological tissue classification with plug-and-play pyramidal deep-broad learning
Published Year: 2022, Cited By: 27
Journal: IEEE Transactions on Medical Imaging
9. External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer
Published Year: 2019, Cited By: 27
Journal: Radiotherapy and Oncology
10. FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections
Published Year: 2020, Cited By: 23
Journal: Medical Physics