3 papers
q-bio.QM2026
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…
q-bio.QM2024
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…
cs.LG2024
Widely Linear Matched Filter: A Lynchpin towards the Interpretability of Complex-valued CNNs
Qingchen Wang, Zhe Li, Zdenka Babic +3
A recent study on the interpretability of real-valued convolutional neural networks (CNNs) {Stankovic_Mandic_2023CNN} has revealed a direct and physically meaningful link with the…