Publications of Jaume Banus

1. Jaume Banus. Heart & Brain. Linking cardiovascular pathologies and neurodegeneration with a combined biophysical and statistical methodology. Theses, Université Côte d'Azur, May 2021. Keyword(s): Variational autoencoder, Personalisation, Gaussian process, Machine learning, Lumped models, Neurodegeneration, Cardiovascular modelling, Medical imaging, Imagerie médicale, Personnalisation, Processus Gaussien, Autoencodeur variationnel, Apprentissage automatique, Modèles regroupés, Neurodégénérescence, Modélisation cardiovasculaire. [bibtex-entry]

1. Jaume Banus, Marco Lorenzi, Oscar Camara, and Maxime Sermesant. Biophysics-based statistical learning: Application to heart and brain interactions. Medical Image Analysis, 72, August 2021. Keyword(s): Heart-Brain interaction, Heart-Brain interaction, Atrial fibrillation, White matter damage, Personalisation, Cardiovascular modelling, Lumped model. [bibtex-entry]

1. Jaume Banus, Maxime Sermesant, Oscar Camara, and Marco Lorenzi. Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets. In MICCAI 2020 - 23th International Conference on Medical Image Computing and Computer Assisted Intervention, Lima / Virtual, Peru, pages 478-486, October 2020. Keyword(s): Variational Inference, Lumped model, Missing features, Biomechanical simulation, Gaussian Process. [bibtex-entry]



2. Jaume Banus, Marco Lorenzi, Oscar Camara, and Maxime Sermesant. Large Scale Cardiovascular Model Personalisation for Mechanistic Analysis of Heart and Brain Interactions. In FIMH 2019 - 10th International Conference on Functional Imaging and Modeling of the Heart, Bordeaux, France, pages 285-293, June 2019. [bibtex-entry]



3. Yingyu Yang, Stephane Gillon, Jaume Banus, Pamela Moceri, and Maxime Sermesant. Non-Invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-driven or Model-based?. In STACOM 2019 - 10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Shenzhen, China, October 2019. Keyword(s): Pulmonary hypertension, Machine learning, Cardiac modelling. [bibtex-entry]

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