Taizhou hospital of Zhejiang Province, China
Author Profile
Early Academic Pursuits:
Ji Wenbin's academic journey started at Wenzhou Medical University, where he earned his Bachelor's degree in Clinical Medicine from 1992 to 1997. This laid the foundation for his specialization in medical imaging and nuclear medicine.
Professional Endeavors:
Following his undergraduate studies,His pursued further education at Zhejiang University, where he completed a Master's degree in Imaging Medicine and Nuclear Medicine from 2002 to 2006. This period marked his entry into the field of medical imaging, setting the stage for his subsequent impactful contributions.
Contributions and Research Focus:
His research focus primarily revolves around the application of advanced imaging techniques, particularly MRI, in diagnosing and prognosticating various medical conditions. His work spans from developing deep learning models for predicting tumor characteristics to evaluating the efficacy of imaging protocols in postoperative care.
Accolades and Recognition:
His contributions to the field of medical imaging have earned him recognition, as evidenced by his publications in reputable journals like Patient Education and Counseling and Radiology. His role as a co-corresponding author on impactful papers highlights his collaborative and leadership skills within interdisciplinary teams.
Impact and Influence:
Through his research and clinical practice as a Chief Physician at Taizhou Hospital affiliated with Wenzhou Medical University, Ji Wenbin has made significant strides in advancing medical imaging techniques' diagnostic accuracy and prognostic value. His work directly impacts patient care and contributes to the continuous improvement of medical practices.
Legacy and Future Contributions:
Looking ahead, Hislegacy includes pioneering research in applying deep learning to medical imaging analysis, particularly in oncology. His ongoing projects and leadership roles indicate a commitment to driving innovation and improving healthcare outcomes. As a respected figure in his field, His future contributions are anticipated to further bridge the gap between cutting-edge technology and clinical application, ultimately benefiting patient care and medical research.
Notable Publications
Using teach-back in patient education to improve patient satisfaction and the clarity of magnetic resonance imaging
Jiang, Y., Wu, Y., Deng, Q., ...Ji, W., Zhang, M.
S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images
Liu, H., Yang, J., Jiang, C., ...Fang, J., Ji, W.
A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma
Yang, J., Dong, X., Wang, F., ...Hu, H., Ji, W.
Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images
Wang, F., Zhan, G., Chen, Q.-Q., ...Chen, Y.-W., Hu, H.-J.
Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma
Dong, X., Yang, J., Zhang, B., ...Gan, M., Ji, W.
Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma
Yang, J., Dong, X., Wang, G., ...Jin, S., Ji, W.
A Comprehensive Model Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Better Predict the Preoperative Histological Grade of Breast Cancer Than a Radiomics Model
Wu, Y., Pan, W., Wang, L., ...Xin, E., Ji, W.
A pyroptosis-related gene signature for prognosis prediction in hepatocellular carcinoma
Chen, Y., Zhu, Y., Dong, Y., ...Yang, S., Ji, W.
18F-FDG PET/CT radiomics signature and clinical parameters predict progression-free survival in breast cancer patients: A preliminary study
Xu, X., Sun, X., Ma, L., ...Xia, X., Lan, X.
Venous gas caused by emphysematous pyelonephritis: a case report and review of literature
Dong, X., You, S., Zhang, H., ...Pang, J., Ji, W.