no code implementations • 3 Jul 2023 • Tobias Goodwin-Allcock, Ting Gong, Robert Gray, Parashkev Nachev, HUI ZHANG
To overcome these limitations, we propose Patch-CNN, a neural network with a minimal (non-voxel-wise) convolutional kernel (3$\times$3$\times$3).
1 code implementation • 27 May 2023 • Guilherme Pombo, Robert Gray, Amy P. K. Nelson, Chris Foulon, John Ashburner, Parashkev Nachev
Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate.
no code implementations • 25 Jan 2023 • Dominic Giles, Robert Gray, Chris Foulon, Guilherme Pombo, Tianbo Xu, H. Rolf Jäger, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, Ashwani Jha, Parashkev Nachev
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials.
no code implementations • 15 Jan 2023 • James K Ruffle, Samia Mohinta, Guilherme Pombo, Robert Gray, Valeriya Kopanitsa, Faith Lee, Sebastian Brandner, Harpreet Hyare, Parashkev Nachev
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology.
no code implementations • 1 Jul 2022 • Tobias Goodwin-Allcock, Jason McEwen, Robert Gray, Parashkev Nachev, HUI ZHANG
A possible consequence of the lack of rotational equivariance is that the training dataset must contain a diverse range of microstucture orientations.
no code implementations • 7 Jun 2022 • Walter H. L. Pinaya, Mark S. Graham, Robert Gray, Pedro F Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H. Mah, Andrew D. MacKinnon, James T. Teo, Rolf Jager, David Werring, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling.
no code implementations • 29 Nov 2021 • Guilherme Pombo, Robert Gray, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, John Ashburner, Parashkev Nachev
The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.
1 code implementation • 24 Nov 2021 • Anthony Bourached, Robert Gray, Xiaodong Guan, Ryan-Rhys Griffiths, Ashwani Jha, Parashkev Nachev
Models of human motion commonly focus either on trajectory prediction or action classification but rarely both.
no code implementations • 21 Jul 2021 • Henry Watkins, Robert Gray, Adam Julius, Yee-Haur Mah, Walter H. L. Pinaya, Paul Wright, Ashwani Jha, Holger Engleitner, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, Rolf Jaeger, Parashkev Nachev
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis.
no code implementations • 23 Feb 2021 • Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic.
2 code implementations • 5 Oct 2020 • Anthony Bourached, Ryan-Rhys Griffiths, Robert Gray, Ashwani Jha, Parashkev Nachev
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD).
no code implementations • 17 Jul 2020 • Jennifer Villareale, Ana Acosta-Ruiz, Samuel Arcaro, Thomas Fox, Evan Freed, Robert Gray, Mathias Löwe, Panote Nuchprayoon, Aleksanteri Sladek, Rush Weigelt, Yifu Li, Sebastian Risi, Jichen Zhu
This paper presents iNNK, a multiplayer drawing game where human players team up against an NN.
1 code implementation • 26 Jul 2019 • Guilherme Pombo, Robert Gray, Tom Varsavsky, John Ashburner, Parashkev Nachev
Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low.
10 code implementations • 11 Sep 2017 • Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I. Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C. Barratt, Sébastien Ourselin, M. Jorge Cardoso, Tom Vercauteren
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.