Search Results for author:
Found 30 papers, 11 papers with code
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology
Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge.
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series.
Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction
We then embed this hybrid model at the latent space of an encoding-decoding framework to enable both supervised and unsupervised identification of the hybrid kinetics and thereby dynamic PET reconstruction.
LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration
The surgical environment imposes unique challenges to the intraoperative registration of organ shapes to their preoperatively-imaged geometry.
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations
Spurious correlation caused by subgroup underrepresentation has received increasing attention as a source of bias that can be perpetuated by deep neural networks (DNNs).
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image.
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting
We compared the presented framework with a comprehensive set of baseline models trained 1) globally on the large meta-training set with diverse dynamics, and 2) individually on single dynamics, both with and without fine-tuning to k-shot support series used by the meta-models.
Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators
Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions.
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.
Neural State-Space Modeling with Latent Causal-Effect Disentanglement
In a novel neural formulation of state-space models (SSMs), we first introduce causal-effect modeling of the latent dynamics via a system of interacting neural ODEs that separately describes 1) the continuous-time dynamics of the internal intervention, and 2) its effect on the trajectory of the system's native state.
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.
Semi-Supervised Learning for Eye Image Segmentation
Recent advances in appearance-based models have shown improved eye tracking performance in difficult scenarios like occlusion due to eyelashes, eyelids or camera placement, and environmental reflections on the cornea and glasses.
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
On three benchmark data sets and one real-world biomedical data set, we demonstrate that this combined regularization results in improved generalization performance of SSL when learning from a small amount of labeled data.
Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
However, many reconstruction problems involve imaging physics that are dependent on the underlying non-Euclidean geometry.
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.
Semi-supervised Medical Image Classification with Global Latent Mixing
In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL.
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.
Analysis of Discriminator in RKHS Function Space for Kullback-Leibler Divergence Estimation
Based on this theory, we then present a scalable way to control the complexity of the discriminator for a reliable estimation of KL divergence.
Progressive Learning and Disentanglement of Hierarchical Representations
By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations.
Venue-based HIV testing at sex work hotspots to reach adolescent girls and young women living with HIV: a cross-sectional study in Mombasa, Kenya
We compared the HIV cascade among AGYW who sell sex (YSW, N=408) versus those who do not (NSW, N=891); and triangulated the potential (100% test acceptance and accuracy) and feasible (accounting for test acceptance and sensitivity) number of AGYW that could be newly diagnosed via hotspot-based HIV rapid testing in Mombasa.
Wavelets to the Rescue: Improving Sample Quality of Latent Variable Deep Generative Models
Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations.
Improving Disentangled Representation Learning with the Beta Bernoulli Process
We note that the independence within and the complexity of the latent density are two different properties we constrain when regularizing the posterior density: while the former promotes the disentangling ability of VAE, the latter -- if overly limited -- creates an unnecessary competition with the data reconstruction objective in VAE.
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space
In this work, we hypothesize -- from the generalization perspective -- that self-ensembling can be improved by exploiting the stochasticity of a disentangled latent space.
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.
Total-Variation Minimization on Unstructured Volumetric Mesh: Biophysical Applications on Reconstruction of 3D Ischemic Myocardium
This paper describes the development and application of a new approach to total-variation (TV) minimization for reconstruction problems on geometrically-complex and unstructured volumetric mesh.
