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Found 8 papers, 3 papers with code
Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images
This raises the question, if we are building patient-specific models based on uncertain measurements, how accurate are the geometries we extract and how can we best represent a patient's vasculature?
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking
The pixel tracking process starts from the end-diastolic frame of the heart cycle using the available manually segmented images to predict the end-systolic segmentation mask.
The Fully Convolutional Transformer for Medical Image Segmentation
To address this shortcoming, we propose The Fully Convolutional Transformer (FCT), which builds on the proven ability of Convolutional Neural Networks to learn effective image representations, and combines them with the ability of Transformers to effectively capture long-term dependencies in its inputs.
Ranked #1 on
Medical Image Segmentation
on ACDC
Fast Parameter Inference in a Biomechanical Model of the Left Ventricle using Statistical Emulation
Emulation methods avoid computationally expensive simulations from the LV model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic.
Applications Methodology
On a New Improvement-Based Acquisition Function for Bayesian Optimization
Bayesian optimization (BO) is a popular algorithm for solving challenging optimization tasks.
Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration
The method is based on a modified annealing path between the posterior distributions of the two models compared, which systematically avoids the high variance prior regime.
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks
Conventional dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption, which is too restrictive in many practical applications.
Non-stationary continuous dynamic Bayesian networks
Dynamic Bayesian networks have been applied widely to reconstruct the structure of regulatory processes from time series data.
