5 citations · 5 across the 5 of their papers we have counts for
5 papers
Characterising cardiac tissue properties with graph neural networks
Ching-En Chiu, Yoo Ri Kim, Magdi Saba +2
Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising a…
Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data
Bastien C. Baluyot, Marta Varela, Chen Qin
Accurate myocardial image registration is essential for cardiac strain analysis and disease diagnosis. However, spectral bias in neural networks impedes modeling high-frequency def…
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions
Ching-En Chiu, Aditi Roy, Sarah Cechnicka +6
Physics-Informed Neural Networks (PINNs) are fast becoming an important tool to solve differential equations rapidly and accurately, and to identify the systems parameters that bes…
Characterisation of Anti-Arrhythmic Drug Effects on Cardiac Electrophysiology using Physics-Informed Neural Networks
Ching-En Chiu, Arieh Levy Pinto, Rasheda A Chowdhury +2
The ability to accurately infer cardiac electrophysiological (EP) properties is key to improving arrhythmia diagnosis and treatment. In this work, we developed a physics-informed n…