NiftyNet: a deep-learning platform for medical imaging. - Eli Gibson; Wenqi Li 001; Carole H. Sudre; Lucas Fidon; Dzhoshkun I. Shakir; Guotai Wang; Zach Eaton-Rosen; Robert J. Gray; Tom Doel; Yipeng Hu; Tom Whyntie; Parashkev Nachev; et al. (CoRR, 2017)

NiftyNet: a deep-learning platform for medical imaging. - Eli Gibson; Wenqi Li 001; Carole H. Sudre; Lucas Fidon; Dzhoshkun I. Shakir; Guotai Wang; Zach Eaton-Rosen; Robert J. Gray; Tom Doel; Yipeng Hu; Tom Whyntie; Parashkev Nachev; et al. (Comput. Methods Programs Biomed., 2018)

Bayesian Volumetric Autoregressive Generative Models for Better Semisupervised Learning. - Guilherme Pombo; Robert J. Gray; Thomas Varsavsky; John Ashburner; Parashkev Nachev (MICCAI (4), 2019)

Generative Model-Enhanced Human Motion Prediction. - Anthony Bourached; Ryan-Rhys Griffiths; Robert J. Gray; Ashwani Jha; Parashkev Nachev (CoRR, 2020)

Unsupervised Brain Anomaly Detection and Segmentation with Transformers. - Walter Hugo Lopez Pinaya; Petru-Daniel Tudosiu; Robert J. Gray; Geraint Rees 001; Parashkev Nachev; Sébastien Ourselin; M. Jorge Cardoso (MIDL, 2021)

An artificial intelligence natural language processing pipeline for information extraction in neuroradiology. - Henry C. Watkins; Robert J. Gray; Ashwani Jha; Parashkev Nachev (CoRR, 2021)

Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion. - Anthony Bourached; Robert J. Gray; Ryan-Rhys Griffiths; Ashwani Jha; Parashkev Nachev (CoRR, 2021)

How Can Spherical CNNs Benefit ML-Based Diffusion MRI Parameter Estimation? - Tobias Goodwin-Allcock; Jason D. McEwen; Robert J. Gray; Parashkev Nachev; Hui Zhang 005 (CDMRI@MICCAI, 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 001; Yee-Haur Mah; Andrew D. MacKinnon; James T. Teo; Hans Rolf Jäger; David Werring; Geraint Rees 001; et al. (MICCAI (8), 2022)

Translating automated brain tumour phenotyping to clinical neuroimaging. - James K. Ruffle; Samia Mohinta; Robert J. Gray; Harpreet Hyare; Parashkev Nachev (CoRR, 2022)

Unsupervised brain imaging 3D anomaly detection and segmentation with transformers. - Walter H. L. Pinaya; Petru-Daniel Tudosiu; Robert J. Gray; Geraint Rees 001; Parashkev Nachev; Sébastien Ourselin; M. Jorge Cardoso (Medical Image Anal., 2022)

Deep forecasting of translational impact in medical research. - Amy P. K. Nelson; Robert J. Gray; James K. Ruffle; Henry C. Watkins; Daniel Herron; Nick Sorros; Danil Mikhailov; M. Jorge Cardoso; Sébastien Ourselin; Nick McNally; Bryan Williams; Geraint E. Rees; et al. (Patterns, 2022)

3d_very_deep_vae. - Robert J. Gray; Matthew Graham; M. Jorge Cardoso; Sébastien Ourselin; Geraint Rees 001; Parashkev Nachev (Zenodo, 2022)

InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data. - Dominic Giles; Robert J. Gray; Chris Foulon; Guilherme Pombo; Tianbo Xu; James K. Ruffle; Hans Rolf Jäger; Manuel Jorge Cardoso; Sébastien Ourselin; Geraint Rees 001; Ashwani Jha; Parashkev Nachev (ML4MHD, 2023)

Deep Variational Lesion-Deficit Mapping. - Guilherme Pombo; Robert J. Gray; Amy P. K. Nelson; Chris Foulon; John Ashburner; Parashkev Nachev (CoRR, 2023)

Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols. - Tobias Goodwin-Allcock; Ting Gong; Robert J. Gray; Parashkev Nachev; Hui Zhang (CoRR, 2023)

The minimal computational substrate of fluid intelligence. - Amy PK Nelson; Joe Mole; Guilherme Pombo; Robert J. Gray; James K. Ruffle; Edgar Chan; Geraint E. Rees; Lisa Cipolotti; Parashkev Nachev (CoRR, 2023)

The legibility of the imaged human brain. - James K. Ruffle; Robert J. Gray; Samia Mohinta; Guilherme Pombo; Chaitanya Kaul; Harpreet Hyare; Geraint Rees 001; Parashkev Nachev (CoRR, 2023)

Compressed representation of brain genetic transcription. - James K. Ruffle; Henry C. Watkins; Robert J. Gray; Harpreet Hyare; Michel Thiebaut de Schotten; Parashkev Nachev (CoRR, 2023)

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models. - Guilherme Pombo; Robert J. Gray; M. Jorge Cardoso; Sébastien Ourselin; Geraint Rees 001; John Ashburner; Parashkev Nachev (Medical Image Anal., 2023)

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)

Computational limits to the legibility of the imaged human brain. - James K. Ruffle; Robert J. Gray; Samia Mohinta; Guilherme Pombo; Chaitanya Kaul; Harpreet Hyare; Geraint Rees 001; Parashkev Nachev (NeuroImage, 2024)

Neuradicon: Operational representation learning of neuroimaging reports. - Henry C. Watkins; Robert J. Gray; Adam Julius; Yee-Haur Mah; James T. Teo; Walter H. L. Pinaya; Paul Wright 001; Ashwani Jha; Holger Engleitner; Jorge Cardoso 002; Sébastien Ourselin; Geraint Rees 001; et al. (Comput. Methods Programs Biomed., 2025)

Deep generative computed perfusion-deficit mapping of ischaemic stroke. - Chayanin Tangwiriyasakul; Pedro Borges; Guilherme Pombo; Stefano Moriconi; Michael S. Elmalem; Paul Wright 001; Yee-Haur Mah; Jane Rondina; Robert J. Gray; Sébastien Ourselin; Parashkev Nachev; M. Jorge Cardoso (CoRR, 2025)

Generalizable automated ischaemic stroke lesion segmentation with vision transformers. - Chris Foulon; Robert J. Gray; James K. Ruffle; Jonathan Best; Tianbo Xu; Henry C. Watkins; Jane Rondina; Guilherme Pombo; Dominic Giles; Paul Wright 001; Marcela Ovando Tellez; Hans Rolf Jäger; et al. (CoRR, 2025)