1 code implementation • 14 Dec 2023 • Christoforos Galazis, Samuel Shepperd, Emma Brouwer, Sandro Queirós, Ebraham Alskaf, Mustafa Anjari, Amedeo Chiribiri, Jack Lee, Anil A. Bharath, Marta Varela
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).
1 code implementation • 21 Dec 2022 • Fatmatulzehra Uslu, Anil A. Bharath
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.
no code implementations • 6 Dec 2022 • Konstantinos Ntagiantas, Eduardo Pignatelli, Nicholas S. Peters, Chris D. Cantwell, Rasheda A. Chowdhury, Anil A. Bharath
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.
no code implementations • 5 May 2022 • Mario Lino, Stati Fotiadis, Anil A. Bharath, Chris Cantwell
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.
no code implementations • 5 May 2022 • Mario Lino, Stathi Fotiadis, Anil A. Bharath, Chris Cantwell
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice.
1 code implementation • 1 Mar 2022 • Christoforos Galazis, Huiyi Wu, Zhuoyu Li, Camille Petri, Anil A. Bharath, Marta Varela
For this task, we propose a new multi-input/output architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature pyRAmid (Tempera).
no code implementations • 9 Jun 2021 • Mario Lino, Chris Cantwell, Anil A. Bharath, Stathi Fotiadis
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.
1 code implementation • ICLR Workshop DeepDiffEq 2019 • Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia, Chris Cantwell, Amos Storkey, Anil A. Bharath
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering.
1 code implementation • 4 Dec 2018 • Wilhelm E. Sorteberg, Stef Garasto, Alison S. Pouplin, Chris D. Cantwell, Anil A. Bharath
In this work, we suggest a neural network capable of understanding a specific physical phenomenon: wave propagation in a two-dimensional medium.
no code implementations • 9 Oct 2018 • Chris D. Cantwell, Yumnah Mohamied, Konstantinos N. Tzortzis, Stef Garasto, Charles Houston, Rasheda A. Chowdhury, Fu Siong Ng, Anil A. Bharath, Nicholas S. Peters
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling.
1 code implementation • 15 Feb 2018 • Antonia Creswell, Anil A. Bharath
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.
no code implementations • 2 Jan 2018 • Antonia Creswell, Alison Pouplin, Anil A. Bharath
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.
no code implementations • ICLR 2018 • Antonia Creswell, Biswa Sengupta, Anil A. Bharath
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.)
1 code implementation • ICLR 2019 • Antonia Creswell, Yumnah Mohamied, Biswa Sengupta, Anil A. Bharath
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.
1 code implementation • 8 Nov 2017 • Antonia Creswell, Anil A. Bharath, Biswa Sengupta
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.)
2 code implementations • 19 Oct 2017 • Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data.
no code implementations • 28 Aug 2017 • Antonia Creswell, Kai Arulkumaran, Anil A. Bharath
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).
no code implementations • 24 Oct 2016 • Christoforos C. Charalambous, Anil A. Bharath
There are several confounding factors that can reduce the accuracy of gait recognition systems.
no code implementations • 27 Sep 2016 • Antonia Creswell, Anil A. Bharath
The cost function used to train a generative model should fit the purpose of the model.
no code implementations • 11 Mar 2015 • Jose Rivera-Rubio, Ioannis Alexiou, Anil A. Bharath
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.