Mark S. Graham

According to our database, Mark S. Graham authored at least 24 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Realistic morphology-preserving generative modelling of the brain.

Petru-Daniel Tudosiu, Walter H. L. Pinaya, Pedro Ferreira Da Costa, Jessica Dafflon, Ashay Patel, Pedro Borges, Virginia Fernandez, Mark S. Graham, Robert J. Gray, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Nat. Mac. Intell., 2024

Generating multi-pathological and multi-modal images and labels for brain MRI.

Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Mark S. Graham, Petru-Daniel Tudosiu, Tom Vercauteren, M. Jorge Cardoso

Medical Image Anal., 2024

2023

Latent Transformer Models for out-of-distribution detection.

Mark S. Graham, Petru-Daniel Tudosiu, Paul Wright, Walter Hugo Lopez Pinaya, Petteri Teikari, Ashay Patel, Jean-Marie U.-King-Im, Yee H. Mah, James T. Teo, Hans Rolf Jäger, David Werring, Geraint Rees, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Medical Image Anal., December, 2023

Generative AI for Medical Imaging: extending the MONAI Framework.

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, Sébastien Ourselin, M. Jorge Cardoso

CoRR, 2023

Unsupervised 3D Out-of-Distribution Detection with Latent Diffusion Models.

Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright, Petru-Daniel Tudosiu, Yee H. Mah, James T. Teo, Hans Rolf Jäger, David Werring, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

A 3D Generative Model of Pathological Multi-modal MR Images and Segmentations.

Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Mark S. Graham, Tom Vercauteren, M. Jorge Cardoso

Proceedings of the Deep Generative Models - Third MICCAI Workshop, 2023

Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking.

Ashay Patel, Petru-Daniel Tudosiu, Walter H. L. Pinaya, Mark S. Graham, Olusola Adeleke, Gary J. Cook, Vicky Goh, Sébastien Ourselin, M. Jorge Cardoso

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Denoising diffusion models for out-of-distribution detection.

Mark S. Graham, Walter H. L. Pinaya, Petru-Daniel Tudosiu, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Transformer-based out-of-distribution detection for clinically safe segmentation.

Mark S. Graham, Petru-Daniel Tudosiu, Paul Wright, Walter Hugo Lopez Pinaya, Jean-Marie U.-King-Im, Yee H. Mah, James T. Teo, Hans Rolf Jäger, David Werring, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain.

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, Sébastien Ourselin, M. Jorge Cardoso

Proceedings of the Simulation and Synthesis in Medical Imaging, 2022

Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models.

Walter H. L. Pinaya, Mark S. Graham, Robert J. Gray, Pedro F. Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H. Mah, Andrew D. MacKinnon, James T. Teo, Hans Rolf Jäger, David Werring, Geraint Rees, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Correction of Susceptibility Distortion in EPI: A Semi-supervised Approach with Deep Learning.

Antoine Legouhy, Mark S. Graham, Michele Guerreri, Whitney Stee, Thomas Villemonteix, Philippe Peigneux, Hui Zhang

Proceedings of the Computational Diffusion MRI - 13th International Workshop, 2022

Can Segmentation Models Be Trained with Fully Synthetically Generated Data?

Virginia Fernandez, Walter Hugo Lopez Pinaya, Pedro Borges, Petru-Daniel Tudosiu, Mark S. Graham, Tom Vercauteren, M. Jorge Cardoso

Proceedings of the Simulation and Synthesis in Medical Imaging, 2022

2020

Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets.

Benjamin Murray, Eric Kerfoot, Mark S. Graham, Carole H. Sudre, Erika Molteni, Liane S. Canas, Michela Antonelli, Alessia Visconti, Andrew T. Chan, Paul W. Franks, Richard Davies, Jonathan Wolf, Tim D. Spector, Claire J. Steves, Marc Modat, Sébastien Ourselin

CoRR, 2020

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE.

Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw, Mark S. Graham, Parashkev Nachev, Sébastien Ourselin, Carole H. Sudre, M. Jorge Cardoso

CoRR, 2020

Test-Time Unsupervised Domain Adaptation.

Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre, Mark S. Graham, Parashkev Nachev, M. Jorge Cardoso

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Hierarchical Brain Parcellation with Uncertainty.

Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sébastien Ourselin, Manuel Jorge Cardoso

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 2020

2019

Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data.

Kanwal K. Bhatia, Mark S. Graham, Louise Terry, Ashley Wood, Paris Tranos, Sameer Trikha, Nicolas Jaccard

CoRR, 2019

2018

A supervised learning approach for diffusion MRI quality control with minimal training data.

Mark S. Graham, Ivana Drobnjak, Hui Zhang

NeuroImage, 2018

Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data.

Jesper L. R. Andersson, Mark S. Graham, Ivana Drobnjak, Hui Zhang, Jon Campbell

NeuroImage, 2018

2017

Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement.

Jesper L. R. Andersson, Mark S. Graham, Ivana Drobnjak, Hui Zhang, Nicola Filippini, Matteo Bastiani

NeuroImage, 2017

2016

Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques.

Mark S. Graham, Ivana Drobnjak, Hui Zhang

NeuroImage, 2016

Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images.

Jesper L. R. Andersson, Mark S. Graham, Eniko Zsoldos, Stamatios N. Sotiropoulos

NeuroImage, 2016

2015

A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRI.

Mark S. Graham, Ivana Drobnjak, Hui Zhang

Proceedings of the Information Processing in Medical Imaging, 2015