Transformer‐based Deep Learning for Rapid Stroke Diagnosis, Qatar-Japan Research Collaboration (QJRC) program (Cycle-2), Qatar, Lead Principal Investigator (2023 - 2025)
Summary:
Strokes are a leading cause of mortality worldwide, and rapid response to stroke recognition is critical to preserving a patient’s life and minimizing brain damage. Qatar is one of the countries that experiences a high incidence of stroke due to hypertension and diabetes. Magnetic Resonance Imaging (MRI) and or Computed Tomography (CT) imaging protocols are used in stroke identification, with MRI being the most commonly used diagnostic method. However, the quality of the imaging protocol dependents on scanning time, and the interpretation of the scan requires the expertise of a radiologist, which may not always be available. This project aims at the development of a machine learning-based ischemic stroke characterization model through collaboration between Qatar University and University of Hyogo. The project’s objectives include establishing an international network, knowledge exchange, and supporting researchers and students affiliated to collaborated teams. The research is expected to have a significant impact on improving skills of young researchers.
