1 citations · 1 across the 11 of their papers we have counts for
11 papers
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…
Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
Swakshar Deb, Nian Wu, Frederick H. Epstein +1
This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magn…
Robust Orthogonal NMF with Label Propagation for Image Clustering
Jingjing Liu, Nian Wu, Xianchao Xiu +1
Non-negative matrix factorization (NMF) is a popular unsupervised learning approach widely used in image clustering. However, in real-world clustering scenarios, most existing NMF…
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…
Learning Geodesics of Geometric Shape Deformations From Images
Nian Wu, Miaomiao Zhang
This paper presents a novel method, named geodesic deformable networks (GDN), that for the first time enables the learning of geodesic flows of deformation fields derived from imag…
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…
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…
NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces
Nian Wu, Miaomiao Zhang
This paper presents NeurEPDiff, a novel network to fast predict the geodesics in deformation spaces generated by a well known Euler-Poincaré differential equation (EPDiff). To achi…
Hybrid Atlas Building with Deep Registration Priors
Nian Wu, Jian Wang, Miaomiao Zhang +3
Registration-based atlas building often poses computational challenges in high-dimensional image spaces. In this paper, we introduce a novel hybrid atlas building algorithm that fa…