4 papers
Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor
Shaheim Ogbomo-Harmitt, Cesare Magnetti, Jakub Grzelak +1
Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is highly disorganised. Convent…
Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models
Shaheim Ogbomo-Harmitt, Cesare Magnetti, Chiara Spota +2
The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using physics-based models such as the bidomai…
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer
Cesare Magnetti, Hadrien Reynaud, Bernhard Kainz
This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging. We utilize Neural Style Transfer to cr…
Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time
Cesare Magnetti, Veronika Zimmer, Nooshin Ghavami +6
We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a fu…