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Found 3 papers, 1 papers with code
Generation of patient specific cardiac chamber models using generative neural networks under a Bayesian framework for electroanatomical mapping
A probabilistic machine learning model trained on a library of CT/MRI scans of the heart can be used during electroanatomical mapping to generate a patient-specific 3D model of the chamber being mapped.
Pruning a neural network using Bayesian inference
Neural network pruning is a highly effective technique aimed at reducing the computational and memory demands of large neural networks.
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh.
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