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Found 20 papers, 9 papers with code
High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D CINE MRI and Unsupervised Neural Networks
We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD).
TMS-Net: A Segmentation Network Coupled With A Run-time Quality Control Method For Robust Cardiac Image Segmentation
Different from previous multi-view networks, TMS-Net has a single encoder and three decoders, leading to better noise robustness, segmentation performance and run-time quality estimation in our experiments on the segmentation of the left atrium on STACOM 2013 and STACOM 2018 challenge datasets.
Estimation of fibre architecture and scar in myocardial tissue using electrograms: an in-silico study
We adapt a wavelet-based surrogate testing analysis to confirm that the inferred conductivity distribution is an accurate representation of the ground truth input to the model.
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice.
REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics
Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces.
Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation
For this task, we propose a new multi-input/output architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature pyRAmid (Tempera).
Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks
Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance often limits application in practice.
Comparing recurrent and convolutional neural networks for predicting wave propagation
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering.
Approximating the solution to wave propagation using deep neural networks
In this work, we suggest a neural network capable of understanding a specific physical phenomenon: wave propagation in a two-dimensional medium.
Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling.
Inverting The Generator Of A Generative Adversarial Network (II)
Using our proposed inversion technique, we are able to identify which attributes of a dataset a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss.
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply.
LatentPoison -- Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.)
Adversarial Information Factorization
We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation.
LatentPoison - Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.)
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data.
On denoising autoencoders trained to minimise binary cross-entropy
When training autoencoders on image data a natural choice of loss function is BCE, since pixel values may be normalised to take values in [0, 1] and the decoder model may be designed to generate samples that take values in (0, 1).
A data augmentation methodology for training machine/deep learning gait recognition algorithms
There are several confounding factors that can reduce the accuracy of gait recognition systems.
Task Specific Adversarial Cost Function
The cost function used to train a generative model should fit the purpose of the model.
Appearance-based indoor localization: A comparison of patch descriptor performance
We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry.
