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  • Currently displaying 141 - 160 of 303 publications
Structure-preserving deep learning
E CELLEDONI, MJ EHRHARDT, C ETMANN, RI MCLACHLAN, B OWREN, C-B SCHONLIEB, F SHERRY
– European Journal of Applied Mathematics
(2021)
32,
888
(doi: 10.1017/s0956792521000139)
Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems
S Mukherjee, O Öktem, C-B Schönlieb
– Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
(2021)
12679 LNCS,
540
(doi: 10.1007/978-3-030-75549-2_43)
A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images
G Wang, X Liu, J Shen, C Wang, Z Li, L Ye, X Wu, T Chen, K Wang, X Zhang, Z Zhou, J Yang, Y Sang, R Deng, W Liang, T Yu, M Gao, J Wang, Z Yang, H Cai, G Lu, L Zhang, L Yang, W Xu, W Wang, A Olvera, I Ziyar, C Zhang, O Li, W Liao, J Liu, W Chen, W Chen, J Shi, L Zheng, L Zhang, Z Yan, X Zou, G Lin, G Cao, LL Lau, L Mo, Y Liang, M Roberts, E Sala, C-B Schönlieb, M Fok, JY-N Lau, T Xu, J He, K Zhang, W Li, T Lin
– Nat Biomed Eng
(2021)
5,
509
(doi: 10.1038/s41551-021-00704-1)
Dynamic spectral residual superpixels
J Zhang, AI Aviles-Rivero, D Heydecker, X Zhuang, R Chan, C-B Schönlieb
– Pattern Recognition
(2021)
112,
107705
(doi: 10.1016/j.patcog.2020.107705)
Mechanisms Underlying Vascular Endothelial Growth Factor Receptor Inhibition-Induced Hypertension The HYPAZ Trial
KM Mäki-Petäjä, A McGeoch, LL Yang, A Hubsch, CM McEniery, PAR Meyer, F Mir, P Gajendragadkar, N Ramenatte, G Anandappa, S Santos Franco, SJ Bond, C-B Schönlieb, Y Boink, C Brune, IB Wilkinson, DI Jodrell, J Cheriyan
– Hypertension (Dallas, Tex. : 1979)
(2021)
77,
1591
(doi: 10.1161/HYPERTENSIONAHA.120.16454)
Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks.
R Ke, A Bugeau, N Papadakis, M Kirkland, P Schuetz, C-B Schonlieb
– IEEE Trans Image Process
(2021)
30,
3555
(doi: 10.1109/tip.2021.3062726)
Machine learning for COVID-19 diagnosis and prognostication: lessons for amplifying the signal whilst reducing the noise
D Driggs, I Selby, M Roberts, E Gkrania-Klotsas, J Rudd, G Yang, J Babar, E Sala, C Schoenlieb
– Radiology Artificial Intelligence
(2021)
3,
e210011
(doi: 10.17863/CAM.74057)
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
C Runkel, C Etmann, M Möller, C-B Schönlieb
(2021)
(link to publication)
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
– 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
– Nature Scientific Reports
(2021)
11,
3499
(doi: 10.1038/s41598-021-82760-w)
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)
(2021)
00,
5246
(doi: 10.1109/icpr48806.2021.9412952)
Learning to Segment Microscopy Images with Lazy Labels
R Ke, A Bugeau, N Papadakis, P Schuetz, C-B Schönlieb
– COMPUTER VISION - ECCV 2020 WORKSHOPS, PT I
(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)
Improving a Stochastic Algorithm for Regularized PET Image Reconstruction
C Delplancke, M Gurnell, J Latz, PJ Markiewicz, C-B Schönlieb, MJ Ehrhardt
– 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference NSS Mic 2020
(2020)
00,
1
(doi: 10.1109/nss/mic42677.2020.9508013)
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)
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)
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
– CoRR
(2020)
abs/1810.10828,
101933
(doi: 10.1016/j.media.2020.101933)
Variational regularisation for inverse problems with imperfect forward operators and general noise models.
L Bungert, M Burger, Y Korolev, C-B Schönlieb
– Inverse Probl
(2020)
abs/2005.14131,
125014
(doi: 10.1088/1361-6420/abc531)
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 Trans Med Imaging
(2020)
39,
4310
(doi: 10.1109/TMI.2020.3017353)

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