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Found 10 papers, 4 papers with code
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning
As test time, metaPNS delivers a personalized neural surrogate by fast feed-forward embedding of a small and flexible number of data available from an individual, achieving -- for the first time -- personalization and surrogate construction for expensive simulations in one end-to-end learning framework.
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning
In this paper, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples.
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
However, by sampling from an approximation of the exact posterior probability density function (pdf) of the parameters, the efficiency is gained at the expense of sampling accuracy.
High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models.
Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
In this paper, we present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space.
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
We introduce a novel model-constrained inference framework that replaces conventional physiological models with a deep generative model trained to generate TMP sequences from low-dimensional generative factors.
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems.
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain.
Improving Generalization of Sequence Encoder-Decoder Networks for Inverse Imaging of Cardiac Transmembrane Potential
The results demonstrate that the generalization ability of an inverse reconstruction network can be improved by constrained stochasticity combined with global aggregation of temporal information in the latent space.
Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia
In this work, we present a conditional variational autoencoder (VAE) to extract the subject-specific adjustment to the ECG data, conditioned on task-specific representations learned from a deterministic encoder.
