Session | ||
1.F: Big Data / Machine Learning I
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Presentations | ||
10:30am - 10:45am
1.F: 1 A computationally efficient deep learning model for high-resolution transient hemodynamics estimation in complex vascular geometries 1Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; 2Siemens Healthineers AG, Forchheim, Germany 10:45am - 11:00am
1.F: 2 Parameter estimation in cardiac biomechanical models based on physics-informed neural networks 1Department of Mathematics and Scientific Computing, NAWI Graz, University of Graz (Austria); 2Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz (Austria); 3BioTechMed-Graz (Austria); 4MOX, Department of Mathematics, Politecnico di Milano (Italy); 5Institute of Mathematics, EPFL (Switzerland) (Professor Emeritus) 11:00am - 11:15am
1.F: 3 Finite volume informed graph attention network for solving partial differential equations — Application to myocardial perfusion 1Inria, Palaiseau, France; 2CentraleSupelec, Inria, Université Paris-Saclay, France; 3HeartFlow Inc., Redwood City, USA; 4ESIEE, Université Gustave Eiffel, France 11:15am - 11:30am
1.F: 4 Machine learning-based models to predict axillary lymph node metastasis in breast cancer patients 1Oncological Pathology and Bioinformatics Research Group, Institut d'Investigació Sanitària Pere i Virgili, Tortosa, Spain; 2Department of Pathology, Hospital de Tortosa Verge de la Cinta, Institut Català de la Salut, Tortosa, Spain; 3Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain; 4BCN MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain 11:30am - 11:45am
1.F: 5 Predicting post-traumatic stress disorder (PTSD) symptoms in women suffering from breast cancer using machine learning 1National Technical University of Athens, Athens, Greece; 2Helsinki University Hospital, Helsinki, Finland |