Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

no code implementations • 13 Oct 2021 • Md Shakil Zaman, Jwala Dhamala, Pradeep Bajracharya, John L. Sapp, B. Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, Linwei Wang

In this paper, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples.

Active Learning

Anatomically-Informed Deep Learning on Contrast-Enhanced Cardiac MRI for Scar Segmentation and Clinical Feature Extraction

no code implementations • 21 Oct 2020 • Haley G. Abramson, Dan M. Popescu, Rebecca Yu, Changxin Lai, Julie K. Shade, Katherine C. Wu, Mauro Maggioni, Natalia A. Trayanova

Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias.

Myocardium Segmentation Segmentation