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  • Currently displaying 161 - 180 of 1297 publications
Variational regularisation for inverse problems with imperfect forward operators and general noise models
L Bungert, M Burger, Y Korolev, C-B Schönlieb
– Inverse Problems
(2020)
abs/2005.14131,
125014
(doi: 10.1088/1361-6420/abc531)
Pericoronary adipose tissue density is associated with clinical disease activity in Takayasu arteritis and coronary arterial inflammation measured by 68Ga-DOTATATE PET in atherosclerosis
C Wall, Y Huang, C Uy, E Le, E Tombetti, D Gopalan, R Manavaki, M Dweck, B Ariff, M Bennett, P Slomka, D Dey, J Mason, J Rudd, J Tarkin
– European Heart Journal
(2020)
41,
182
(doi: 10.1093/ehjci/ehaa946.0182)
Contrast CT classification of asymptomatic and symptomatic carotids in stroke and transient ischaemic attack with deep learning and interpretability
EPV Le, NR Evans, JM Tarkin, MM Chowdhury, F Zaccagna, C Wall, Y Huang, JR Weir-Mccall, C Chen, EA Warburton, CB Schonlieb, E Sala, JHF Rudd
– European Heart Journal
(2020)
41,
ehaa946.2418
(doi: 10.1093/ehjci/ehaa946.2418)
Radiomics applied to carotid CT angiograms can identify significant differences between culprit and non-culprit lesions in patients with stroke and transient ischaemic attack
E Le, NR Evans, JM Tarkin, MM Chowdhury, F Zaccagna, H Pavey, B Ganeshan, C Wall, Y Huang, JR Weir-Mccall, EA Warburton, CB Schonlieb, E Sala, JHF Rudd
– European Heart Journal
(2020)
41,
2417
(doi: 10.1093/ehjci/ehaa946.2417)
Higher-Order Total Directional Variation: Imaging Applications.
S Parisottoy, J Lellmannz, S Masnoux, CB Schönlieb
– SIAM J. Imaging Sci.
(2020)
13,
2063
(doi: 10.1137/19M1239209)
Five rules for evidence communication.
M Blastland, ALJ Freeman, S van der Linden, TM Marteau, D Spiegelhalter
– Nature
(2020)
587,
362
(doi: 10.1038/d41586-020-03189-1)
Scanning electron diffraction tomography of strain
R Tovey, DN Johnstone, SM Collins, WRB Lionheart, PA Midgley, M Benning, C-B Schönlieb
– Inverse Problems
(2020)
37,
015003
(doi: 10.1088/1361-6420/abc961)
A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19
S Wang, X Yang, L Li, P Nadler, R Arcucci, Y Huang, Z Teng, Y Guo
– IEEE Computational Intelligence Magazine
(2020)
15,
23
(doi: 10.1109/mci.2020.3019874)
Preface for the special issue ‘Variational methods and effective algorithms for imaging and vision’
CB Schonlieb, M Hintermuller, S Arridge
– Inverse Problems
(2020)
36,
110401
(doi: 10.1088/1361-6420/abb8e9)
Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study
AK Clift, CAC Coupland, RH Keogh, K Diaz-Ordaz, E Williamson, EM Harrison, A Hayward, H Hemingway, P Horby, N Mehta, J Benger, K Khunti, D Spiegelhalter, A Sheikh, J Valabhji, RA Lyons, J Robson, MG Semple, F Kee, P Johnson, S Jebb, T Williams, J Hippisley-Cox
– The BMJ
(2020)
371,
m3731
(doi: 10.1136/bmj.m3731)
Communicating personalised risks from COVID-19: guidelines from an empirical study
A Freeman, J Kerr, G Recchia, C Schneider, ACE Lawrence, L Finikarides, G Luoni, S Dryhurst, D Spiegelhalter
(2020)
2020.10.05.20206961
(doi: 10.1101/2020.10.05.20206961)
Accelerating variance-reduced stochastic gradient methods
D Driggs, MJ Ehrhardt, CB Schönlieb
– Mathematical Programming
(2020)
191,
671
(doi: 10.1007/s10107-020-01566-2)
Use of "normal" risk to improve understanding of dangers of covid-19
D Spiegelhalter
– BMJ (Clinical research ed.)
(2020)
370,
m3259
(doi: 10.1136/bmj.m3259)
OP5 Pericoronary adipose tissue density is greater in takayasu arteritis than atherosclerosis and is associated with coronary arterial inflammation measured by 68Ga-DOTATATE PET
C Wall, Y Huang, C Uy, EPV Le, E Tombetti, R Manavaki, MR Dweck, D Gopalan, MR Bennett, P Slomka, D Dey, JC Mason, JHF Rudd, JM Tarkin
– Scientific oral presentations
(2020)
106,
A2
(doi: 10.1136/heartjnl-2020-BSCI.5)
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems
C Etmann, R Ke, CB Schonlieb
– PROCEEDINGS OF THE 2020 IEEE 30TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
(2020)
2020-September,
1
(doi: 10.1109/MLSP49062.2020.9231874)
A quantitative framework for exploring exit strategies from the COVID-19 lockdown.
AS Fokas, J Cuevas-Maraver, PG Kevrekidis
– Chaos, solitons, and fractals
(2020)
140,
110244
(doi: 10.1016/j.chaos.2020.110244)
Representation and reconstruction of covariance operators in linear inverse problems
E Lila, S Arridge, JAD Aston
– Inverse Problems
(2020)
36,
085002
(doi: 10.1088/1361-6420/ab8713)
Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.
AS Fokas, N Dikaios, GA Kastis
– Journal of the Royal Society Interface
(2020)
17,
20200494
(doi: 10.1098/rsif.2020.0494)
Fokas method for linear boundary value problems involving mixed spatial derivatives.
A Batal, AS Fokas, T Özsarı
– Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
(2020)
476,
20200076
(doi: 10.1098/rspa.2020.0076)
The Modified Helmholtz Equation on a Regular Hexagon - The Symmetric Dirichlet Problem.
K Kalimeris, AS Fokas
– Axioms
(2020)
9,
89
(doi: 10.3390/AXIOMS9030089)

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