no code implementations • 5 Apr 2024 • Chayanin Tangwiriyasakul, Pedro Borges, Stefano Moriconi, Paul Wright, Yee-Haur Mah, James Teo, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Stroke is a leading cause of disability and death.
no code implementations • 3 Apr 2024 • James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau, Rebekah Rajiah, Faith Lee, Sebastian Brandner, Parashkev Nachev, Harpreet Hyare
We quantified: 1) agreement across neuroradiologists and VASARI-auto; 2) calibration of performance equity; 3) an economic workforce analysis; and 4) fidelity in predicting patient survival.
no code implementations • 24 Nov 2023 • M. Jorge Cardoso, Julia Moosbauer, Tessa S. Cook, B. Selnur Erdal, Brad Genereaux, Vikash Gupta, Bennett A. Landman, Tiarna Lee, Parashkev Nachev, Elanchezhian Somasundaram, Ronald M. Summers, Khaled Younis, Sebastien Ourselin, Franz MJ Pfister
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology.
no code implementations • 24 Oct 2023 • James K Ruffle, Henry Watkins, Robert J Gray, Harpreet Hyare, Michel Thiebaut de Schotten, Parashkev Nachev
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space.
1 code implementation • 23 Aug 2023 • James K Ruffle, Robert J Gray, Samia Mohinta, Guilherme Pombo, Chaitanya Kaul, Harpreet Hyare, Geraint Rees, Parashkev Nachev
It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.
no code implementations • 14 Aug 2023 • Amy PK Nelson, Joe Mole, Guilherme Pombo, Robert J Gray, James K Ruffle, Edgar Chan, Geraint E Rees, Lisa Cipolotti, Parashkev Nachev
The quantification of cognitive powers rests on identifying a behavioural task that depends on them.
2 code implementations • 27 Jul 2023 • Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
1 code implementation • 7 Jul 2023 • Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright, Petru-Daniel Tudosiu, Yee H. Mah, James T. Teo, H. Rolf Jäger, David Werring, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs).
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.
1 code implementation • 14 Nov 2022 • Mark S. Graham, Walter H. L. Pinaya, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs.
no code implementations • 1 Nov 2022 • Michael S. Elmalem, Hanna Moody, James K. Ruffle, Michel Thiebaut de Schotten, Patrick Haggard, Beate Diehl, Parashkev Nachev, Ashwani Jha
Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.
1 code implementation • 15 Sep 2022 • Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images.
1 code implementation • 7 Sep 2022 • Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso
Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.
no code implementations • 25 Jul 2022 • Robert Carruthers, Isabel Straw, James K Ruffle, Daniel Herron, Amy Nelson, Danilo Bzdok, Delmiro Fernandez-Reyes, Geraint Rees, Parashkev Nachev
Equity is widely held to be fundamental to the ethics of healthcare.
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.
1 code implementation • 13 Jun 2022 • Mikael Brudfors, Yael Balbastre, John Ashburner, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Data used in image segmentation are not always defined on the same grid.
no code implementations • 13 Jun 2022 • James K Ruffle, Samia Mohinta, Robert J Gray, Harpreet Hyare, Parashkev Nachev
This is a task contemporary machine learning could facilitate, especially in neuroimaging, but its ability to deal with incomplete data common in real world clinical practice remains unknown.
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.
1 code implementation • 21 May 2022 • Mark S Graham, Petru-Daniel Tudosiu, Paul Wright, Walter Hugo Lopez Pinaya, U Jean-Marie, Yee Mah, James Teo, Rolf H Jäger, David Werring, Parashkev Nachev, Sebastien Ourselin, M Jorge Cardoso
We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD.
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 • 17 Oct 2021 • Amy PK Nelson, Robert J Gray, James K Ruffle, Henry C Watkins, Daniel Herron, Nick Sorros, Danil Mikhailov, M. Jorge Cardoso, Sebastien Ourselin, Nick McNally, Bryan Williams, Geraint E. Rees, Parashkev Nachev
We show that citations are only moderately predictive of translational impact as judged by inclusion in patents, guidelines, or policy documents.
no code implementations • 29 Sep 2021 • James F Cann, Timothy J Roberts, Amy R Tso, Amy Nelson, Parashkev Nachev
Sparse sequential highly-multivariate data of the form characteristic of hospital in-patient investigation and treatment poses a considerable challenge for representation learning.
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.
1 code implementation • 12 Apr 2021 • Mikael Brudfors, Yaël Balbastre, John Ashburner, Geraint Rees, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data.
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.
no code implementations • 5 Oct 2020 • Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre, Mark S. Graham, Parashkev Nachev, M. Jorge Cardoso
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain).
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 • 16 Sep 2020 • Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree.
no code implementations • 3 Jun 2020 • Mikael Brudfors, Yaël Balbastre, Guillaume Flandin, Parashkev Nachev, John Ashburner
We describe a diffeomorphic registration algorithm that allows groups of images to be accurately aligned to a common space, which we intend to incorporate into the SPM software.
no code implementations • MIDL 2019 • Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw, Mark Graham, Parashkev Nachev, Sebastien Ourselin, Carole H. Sudre, M. Jorge Cardoso
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.
no code implementations • 22 Oct 2019 • Anthony Bourached, Parashkev Nachev
Animal behaviour is complex and the amount of data in the form of video, if extracted, is copious.
2 code implementations • 3 Sep 2019 • Mikael Brudfors, Yael Balbastre, Parashkev Nachev, John Ashburner
The model-driven nature of the approach means that no type of training is needed for applicability to the diversity of MR contrasts present in a clinical context.
no code implementations • 16 Aug 2019 • Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
no code implementations • 16 Aug 2019 • Mikael Brudfors, John Ashburner, Parashkev Nachev, Yael Balbastre
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications.
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.
4 code implementations • 8 Oct 2018 • Mikael Brudfors, Yael Balbastre, Parashkev Nachev, John Ashburner
This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast.
no code implementations • 14 Sep 2018 • Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference.
no code implementations • 17 Jul 2018 • Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre, Parashkev Nachev, M. Jorge Cardoso
In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality.
no code implementations • 8 Jun 2018 • Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso
The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale.
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.