1 citations · 3 across the 15 of their papers we have counts for
16 papers
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery
Zhikang Xu, Jiarui Xing, Jian Wang
We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridges statistical robustness and…
Geodesic-informed Generative Diffusion Model For Topology-preserved Image Video Generation
Nian Wu, Nivetha Jayakumar, Jiarui Xing +1
Generative diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, reconstruction, and segmentatio…
Deep Image Prototype Learning with Geometric Heat-Kernel Priors
Jiarui Xing, Tal Zeevi, Nian Wu +1
Learning unsupervised representations of medical imaging cohorts can reveal anatomically meaningful prototypes without expert labels, which are often noisy and fail to capture true…
Uncertainty in a Single Pass: A Closed-Form Identity for One-Step Flow Matching
Jiarui Xing, Song Wang, Jian Wang
Flow matching provides a highly effective framework for generative modeling, yet estimating the uncertainty of its generated samples remains a fundamental challenge. Existing metho…
PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion
Jian Wang, Sixing Rong, Jiarui Xing +2
We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis that reformulates imaging-pathology as a disentangled additive deviation on…
IGG: Image Generation Informed by Geodesic Dynamics in Deformation Spaces
Nian Wu, Nivetha Jayakumar, Jiarui Xing +1
Generative models have recently gained increasing attention in image generation and editing tasks. However, they often lack a direct connection to object geometry, which is crucial…
TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration
Nian Wu, Jiarui Xing, Miaomiao Zhang
This paper presents a novel approach, termed {\em Temporal Latent Residual Network (TLRN)}, to predict a sequence of deformation fields in time-series image registration. The chall…
LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation
Jiarui Xing, Nivetha Jayakumar, Nian Wu +3
Motion and deformation analysis of cardiac magnetic resonance (CMR) imaging videos is crucial for assessing myocardial strain of patients with abnormal heart functions. Recent adva…
Assessment of Clonal Hematopoiesis of Indeterminate Potential and Future Cardiomyopathy from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population
Jiarui Xing, Sangeon Ryu, Shawn Ahn +7
We propose a novel deep learning framework to identify clonal hematopoiesis of indeterminate potential (CHIP), a somatic mutation condition associated with adverse cardiovascular o…
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…
MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes
Jian Wang, Jiarui Xing, Jason Druzgal +2
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous lear…
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…
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…
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…