Search Results for author: Derek K. Jones

Found 5 papers, 3 papers with code

Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study

1 code implementation2 Aug 2022 Matteo Mancini, Derek K. Jones, Marco Palombo

In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, with proof-of-concept application to 4D diffusion-weighted MRI (dMRI).

Data Compression

Q-space quantitative diffusion MRI measures using a stretched-exponential representation

1 code implementation15 Sep 2020 Tomasz Pieciak, Maryam Afzali, Fabian Bogusz, Aja-Fernández, Derek K. Jones

Diffusion magnetic resonance imaging (dMRI) is a relatively modern technique used to study tissue microstructure in a non-invasive way.

Tractometry-based Anomaly Detection for Single-subject White Matter Analysis

no code implementations MIDL 2019 Maxime Chamberland, Sila Genc, Erika P. Raven, Greg D. Parker, Adam Cunningham, Joanne Doherty, Marianne van den Bree, Chantal M. W. Tax, Derek K. Jones

There is an urgent need for a paradigm shift from group-wise comparisons to individual diagnosis in diffusion MRI (dMRI) to enable the analysis of rare cases and clinically-heterogeneous groups.

Anomaly Detection

q-Space Novelty Detection with Variational Autoencoders

1 code implementation8 Jun 2018 Aleksei Vasilev, Vladimir Golkov, Marc Meissner, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K. Jones, Daniel Cremers

Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output.

Novelty Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.