GMPAS
Project Overview
University
Year
About
Glioma treatment relies on molecular biomarkers that usually require an invasive biopsy and specialist genetic testing, delaying information needed for clinical decisions. GMPAS uses deep learning to analyse standard MRI scans and predict key tumour biomarkers and patient prognosis before surgery. It separates biological patterns from scanner noise, compares new scans with similar historical cases and produces visual maps explaining its predictions. By providing earlier, lower-cost decision support, it could widen access to personalised brain tumour care and help clinicians plan surgery and treatment.
Team
Yu Qiao
YU QIAO was born in Jining, China, in 1990. He received the M.S. degree in software engineering from the Shandong University of Science and Technology, Qingdao, in 2016. He is currently pursuing the Ph.D. degree in computer science from Taylor's University, Malaysia. Since 2017, he has been a lecturer in the Department of Computer Science at Jining Polytechnic. His research interests primarily include the design of deep learning algorithms, AI-assisted glioma treatment, and genotype classification of gliomas based on 3D MRI. He has also contributed to the writing of several books related to artificial intelligence.