9 citations · 17 across the 8 of their papers we have counts for
9 papers
Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments
Yubo Ye, Sweekar Piya, Xiajun Jiang +1
Learning latent dynamics is central to assessing the current states and forecasting the future trajectories of high-dimensional time series. For locally-stationary latent dynamics…
CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations
Ryan Missel, Xiajun Jiang, Linwei Wang
Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art solutions, they typically add…
Federated Foundation Model for GI Endoscopy Images
Alina Devkota, Annahita Amireskandari, Joel Palko +5
Gastrointestinal (GI) endoscopy is essential in identifying GI tract abnormalities in order to detect diseases in their early stages and improve patient outcomes. Although deep lea…
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology
Xiajun Jiang, Sumeet Vadhavkar, Yubo Ye +3
Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challeng…
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
Yubo Ye, Sumeet Vadhavkar, Xiajun Jiang +3
Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many…
LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration
Dingrong Wang, Soheil Azadvar, Jon Heiselman +3
The surgical environment imposes unique challenges to the intraoperative registration of organ shapes to their preoperatively-imaged geometry. Biomechanical model-based registratio…
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning
Xiajun Jiang, Zhiyuan Li, Ryan Missel +6
Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surr…
Neural State-Space Modeling with Latent Causal-Effect Disentanglement
Maryam Toloubidokhti, Ryan Missel, Xiajun Jiang +2
Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due…
Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
Xiajun Jiang, Sandesh Ghimire, Jwala Dhamala +3
Deep neural networks have shown great potential in image reconstruction problems in Euclidean space. However, many reconstruction problems involve imaging physics that are dependen…