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12/21/2022
TMS-Net: A Segmentation Network Coupled With A Run-time Quality Control Method For Robust Cardiac Image Segmentation
Recently, deep networks have shown impressive performance for the segmen...
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12/06/2022
Estimating Cardiac Tissue Conductivity from Electrograms with Fully Convolutional Networks
Atrial Fibrillation (AF) is characterized by disorganised electrical act...
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05/05/2022
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
Numerical simulators are essential tools in the study of natural fluid-s...
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03/01/2022
Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation
Assessing the structure and function of the right ventricle (RV) is impo...
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06/09/2021
Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks
Continuum mechanics simulators, numerically solving one or more partial ...
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02/20/2020
Comparing recurrent and convolutional neural networks for predicting wave propagation
Dynamical systems can be modelled by partial differential equations and ...
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12/04/2018
Approximating the solution to wave propagation using deep neural networks
Humans gain an implicit understanding of physical laws through observing...
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10/09/2018
Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
We review some of the latest approaches to analysing cardiac electrophys...
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02/15/2018
Inverting The Generator Of A Generative Adversarial Network (II)
Generative adversarial networks (GANs) learn a deep generative model tha...
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01/02/2018
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
We propose a novel deep learning model for classifying medical images in...
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11/14/2017
Conditional Autoencoders with Adversarial Information Factorization
Generative models, such as variational auto-encoders (VAE) and generativ...
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11/08/2017
LatentPoison - Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined...
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10/19/2017
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep repre...
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08/28/2017
On denoising autoencoders trained to minimise binary cross-entropy
Denoising autoencoders (DAEs) are powerful deep learning models used for...
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10/24/2016
A data augmentation methodology for training machine/deep learning gait recognition algorithms
There are several confounding factors that can reduce the accuracy of ga...
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09/27/2016
Task Specific Adversarial Cost Function
The cost function used to train a generative model should fit the purpos...
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03/11/2015