13 citations · 41 across the 7 of their papers we have counts for
7 papers
High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network
Christoforos Galazis, Anil Anthony Bharath, Marta Varela
Functional analysis of the left atrium (LA) plays an increasingly important role in the prognosis and diagnosis of cardiovascular diseases. Echocardiography-based measurements of L…
Prototype of a Cardiac MRI Simulator for the Training of Supervised Neural Networks
Marta Varela, Anil A Bharath
Supervised deep learning methods typically rely on large datasets for training. Ethical and practical considerations usually make it difficult to access large amounts of healthcare…
EP-PINNs: Cardiac Electrophysiology Characterisation using Physics-Informed Neural Networks
Clara Herrero Martin, Alon Oved, Rasheda A Chowdhury +4
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of a…
Inverting The Generator Of A Generative Adversarial Network
Antonia Creswell, Anil Anthony Bharath
Generative adversarial networks (GANs) learn to synthesise new samples from a high-dimensional distribution by passing samples drawn from a latent space through a generative networ…
A data augmentation methodology for training machine/deep learning gait recognition algorithms
Christoforos C. Charalambous, Anil A. Bharath
There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characte…
Improving Sampling from Generative Autoencoders with Markov Chains
Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders a…