Xiangya Hospital of Central South University China
Fangxue Yang is a dedicated PhD candidate in Medicine at Xiangya Hospital, Central South University, China. 🎓 Her research is rooted in neuroradiology, with a strong focus on brain function and structure imaging. She brings together clinical experience and biomedical research, especially in MRI-based analysis, to support early diagnosis and personalized treatment approaches. Aiming to bridge the clinical-research divide, she contributes actively to academic discussions, peer-reviewed publications, and collaborative projects. 🧠🔍
Profile
Scopus
🎓 Education
Fangxue Yang is currently pursuing her Doctorate (PhD) in Medicine at Central South University, focusing on neuroradiology. Her academic foundation is grounded in clinical medicine, complemented by strong expertise in advanced imaging modalities such as MRI and deep learning applications in medical diagnostics.
🏥 Experience
With a balanced background in both clinical radiology and biomedical research, Fangxue Yang has cultivated hands-on experience in neuroimaging and MRI-based diagnostics. As a researcher at the Department of Radiology, Xiangya Hospital, she works closely with multidisciplinary teams on projects involving deep learning and imaging informatics. Her contributions support early detection of neurological disorders, with an emphasis on integrating AI with radiological practice. 🧑⚕️💻
🔬 Research Interest
Her research interests lie at the intersection of neuroradiology, brain imaging, and artificial intelligence in medical diagnostics. Specifically, she is focused on the early detection of radiation-induced brain injury using deep learning models and automated MRI analysis. Her vision is to enhance precision medicine through AI-driven solutions and imaging informatics. 🧠🤖🖼️
🏆 Awards
Fangxue Yang is a nominee for the Best Researcher Award 🏅 in recognition of her impactful contributions to AI-assisted neuroradiology and her commitment to translational research. Her innovative work continues to earn attention from academic and clinical communities, setting the stage for future accolades in medical imaging and radiological science. 🌟
📚 Publications Top Notes:
Automated deep learning-assisted early detection of radiation-induced temporal lobe injury on MRI: a multicenter retrospective analysis
📌 Published in: European Radiology (2025)
📈 Cited by articles: View on SpringerLink
White matter functional and structural alterations of spinocerebellar ataxia type 3: A longitudinal MRI study