activity
20192022
most citedDeep Networks to Automatically Detect Late-activating Regions of the Heart

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2022

Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI

Jiarui Xing, Shuo Wang, Kenneth C. Bilchick +2

Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those relat…

eess.IV2022

Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI

Jiarui Xing, Shuo Wang, Kenneth C. Bilchick +3

The selection of an optimal pacing site, which is ideally scar-free and late activated, is critical to the response of cardiac resynchronization therapy (CRT). Despite the success…

eess.IV20201 cited

Deep Networks to Automatically Detect Late-activating Regions of the Heart

Jiarui Xing, Sona Ghadimi, Mohammad Abdishektaei +3

This paper presents a novel method to automatically identify late-activating regions of the left ventricle from cine Displacement Encoding with Stimulated Echo (DENSE) MR images. W…

cs.LG2019

Mixture Probabilistic Principal Geodesic Analysis

Youshan Zhang, Jiarui Xing, Miaomiao Zhang

Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures. While probabilistic principal geodesic analysis~(PPGA) has been propos…

eess.IV2019

Plug-and-Play Priors for Reconstruction-based Placental Image Registration

Jiarui Xing, Ulugbek Kamilov, Wenjie Wu +2

This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Rece…