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  • Currently displaying 121 - 140 of 269 publications
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, AI Aviles-Rivero, C Etmann, C McCague, L Beer, JR Weir-McCall, Z Teng, E Gkrania-Klotsas, A Ruggiero, A Korhonen, E Jefferson, E Ako, G Langs, G Gozaliasl, G Yang, H Prosch, J Preller, J Stanczuk, J Tang, J Hofmanninger, J Babar, LE Sánchez, M Thillai, PM Gonzalez, P Teare, X Zhu, M Patel, C Cafolla, H Azadbakht, J Jacob, J Lowe, K Zhang, K Bradley, M Wassin, M Holzer, K Ji, MD Ortet, T Ai, N Walton, P Lio, S Stranks, T Shadbahr, W Lin, Y Zha, Z Niu, JHF Rudd, E Sala, CB Schönlieb
– Nature Machine Intelligence
(2021)
3,
199
(doi: 10.1038/s42256-021-00307-0)
Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
EPV Le, L Rundo, JM Tarkin, NR Evans, MM Chowdhury, PA Coughlin, H Pavey, C Wall, F Zaccagna, FA Gallagher, Y Huang, R Sriranjan, A Le, JR Weir-McCall, M Roberts, FJ Gilbert, EA Warburton, C-B Schönlieb, E Sala, JHF Rudd
– Scientific reports
(2021)
11,
3499
(doi: 10.1038/s41598-021-82760-w)
Learning to Segment Microscopy Images with Lazy Labels
R Ke, A Bugeau, N Papadakis, P Schuetz, CB Schönlieb
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2021)
12535,
411
(doi: 10.1007/978-3-030-66415-2_27)
Machine churning
M Roberts
– NEW SCIENTIST
(2021)
245,
23
(link to publication)
Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination.
D Kimanius, G Zickert, T Nakane, J Adler, S Lunz, C-B Schönlieb, O Öktem, SHW Scheres
– IUCrJ
(2021)
8,
60
(doi: 10.1107/S2052252520014384)
Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution
V Corona, AI Aviles-Rivero, N Debroux, CL Guyader, C-B Schönlieb
– Medical Image Analysis
(2020)
68,
101941
(doi: 10.1016/j.media.2020.101941)
Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction.
AI Aviles-Rivero, N Debroux, G Williams, MJ Graves, C-B Schönlieb
– Medical image analysis
(2020)
68,
101933
(doi: 10.1016/j.media.2020.101933)
Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction.
J Liu, AI Aviles-Rivero, H Ji, C-B Schönlieb
– Medical Image Analysis
(2020)
68,
101930
(doi: 10.1016/j.media.2020.101930)
Learning the Sampling Pattern for MRI.
F Sherry, M Benning, JC De Los Reyes, MJ Graves, G Maierhofer, G Williams, C-B Schonlieb, MJ Ehrhardt
– IEEE transactions on medical imaging
(2020)
39,
4310
(doi: 10.1109/tmi.2020.3017353)
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)
36,
125014
(doi: 10.1088/1361-6420/abc531)
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)
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)
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)
Accelerating variance-reduced stochastic gradient methods
D Driggs, MJ Ehrhardt, CB Schönlieb
– Mathematical Programming
(2020)
191,
671
(doi: 10.1007/s10107-020-01566-2)
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)
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, AI Aviles-Rivero, C Etmann, C McCague, L Beer, JR Weir-McCall, Z Teng, E Gkrania-Klotsas, JHF Rudd, E Sala, C-B Schönlieb
(2020)
(doi: 10.48550/arxiv.2008.06388)
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
C Runkel, S Dorenkamp, H Bauermeister, M Moeller
– 2020 25th International Conference on Pattern Recognition (ICPR)
(2020)
00,
5246
(doi: 10.1109/icpr48806.2021.9412952)
Tomographic reconstruction with spatially varying parameter selection
Y Dong, CB Schönlieb
– Inverse Problems
(2020)
36,
054002
(doi: 10.1088/1361-6420/ab72d4)
Optical flow analysis reveals that Kinesin-mediated advection impacts the orientation of microtubules in theDrosophilaoocyte
M Drechsler, LF Lang, L Al-Khatib, H Dirks, M Burger, C-B Schönlieb, IM Palacios
– Molecular biology of the cell
(2020)
31,
1246
(doi: 10.1091/MBC.E19-08-0440)
Variational Osmosis for Non-Linear Image Fusion
S Parisotto, L Calatroni, A Bugeau, N Papadakis, C-B Schonlieb
– CoRR
(2020)
abs/1910.02012,
5507
(doi: 10.1109/tip.2020.2983537)

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