Imperial College London / The Imperial College of Science, Technology and Medicine
University / College
Location:
London,
United Kingdom (GB)
ISNI: 0000000121138111
ROR: https://ror.org/041kmwe10
Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis (2018)
Cajanus A, Hall A, Koikkalainen J, Solje E, Tolonen A, Urhemaa T, Liu Y, et al.
Journal article
Modelling the progression of Alzheimer's disease in MRI using generative adversarial networks (2018)
Bowles C, Gunn R, Hammers A, Rueckert D
Conference contribution
Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations (2018)
Bai W, Suzuki H, Qin C, Tarroni G, Oktay O, Matthews PM, Rueckert D
Conference contribution
Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension (2018)
Duan J, Schlemper J, Bai W, Dawes TJW, Bello G, Doumou G, De Marvao A, et al.
Conference contribution
Bayesian deep learning for accelerated mr image reconstruction (2018)
Schlemper J, Castro DC, Bai W, Qin C, Oktay O, Duan J, Price AN, et al.
Conference contribution
Cardiac mr motion artefact correction from k-space using deep learning-based reconstruction (2018)
Oksuz I, Clough J, Bustin A, Cruz G, Prieto C, Botnar R, Rueckert D, et al.
Conference contribution
Joint learning of motion estimation and segmentation for cardiac MR image sequences (2018)
Qin C, Bai W, Schlemper J, Petersen SE, Piechnik SK, Neubauer S, Rueckert D
Conference contribution
Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI (2018)
Schlemper J, Yang G, Ferreira P, Scott A, Mcgill LA, Khalique Z, Gorodezky M, et al.
Conference contribution
A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks (2018)
Tarroni G, Oktay O, Sinclair M, Bai W, Schuh A, Suzuki H, De Marvao A, et al.
Conference contribution
Cardiac MR segmentation from undersampled k-space using deep latent representation learning (2018)
Schlemper J, Oktay O, Bai W, Castro DC, Duan J, Qin C, Hajnal JV, Rueckert D
Conference contribution