1 citations · 2 across the 5 of their papers we have counts for
5 papers
Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images
Jiarui Xing, Nian Wu, Kenneth Bilchick +2
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acqu…
CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic phenotypes using learned multi-valued decision diagrams
Josephine Lamp, Yuxin Wu, Steven Lamp +4
Early identification of high risk heart failure (HF) patients is key to timely allocation of life-saving therapies. Hemodynamic assessments can facilitate risk stratification and e…
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…
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…
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…