Semi-automated Quantitative tissue characterization by 3D-Cardiovascular Magnetic Resonance - 3D-Quanti
Richard Hickstein, Halil Noyan, Charité - Universitätsmedizin Berlin, Experimental Clinical Research Center, ECRC eine gemeinsame Einrichtung von Charité und Max Delbrück Zentrum, Campus Buch Charité
Contents
Learning Outcomes Practical research experience in a multidisciplinary research environment consisting of medical and technical researchers. Content Supervised multidisciplinary research project applying machine-learning (ML) and web-application development to 3D-quantitative tissue characterization in cardiovascular magnetic resonance imaging (CMR) for healthy-cohorts and cardiomyopathy patients. Description of Teaching and Learning Methods • Structured Onboarding Phase • Supervised Clinical and Tech Tracks • weekly meetings/discussions between groups • Clinical: Image analysis and User Feedback • Tech: Model and App Development Desirable prerequisites for participation in the course: The course is aimed at students with a biomedical (medicine, biotech, biology) or technical (computer science, electrical engineering, maths, physics etc.) background. Clinical Track: Basic experience in Basic Cardiology (Modul 11) Tech Track: Experience in ML&Web-Frameworks (PyTorch, Python-Flask) & JavaScript
Subject-specific supervisor
Univ.-Prof. Dr. med. Jeanette Schulz-Menger
Contact
richard.hickstein@charite.de
Link to the course catalog
here#mce_temp_url#