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  • Currently displaying 161 - 180 of 329 publications
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
M van Eijnatten, L Rundo, KJ Batenburg, F Lucka, E Beddowes, C Caldas, FA Gallagher, E Sala, C-B Schönlieb, R Woitek
– Computer methods and programs in biomedicine
(2021)
208,
106261
Efficient Global Optimization of Non-Differentiable, Symmetric Objectives for Multi Camera Placement
ML Hänel, C-B Schönlieb
– IEEE Sensors Journal
(2021)
22,
5278
Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework
M Benning, MM Betcke, MJ Ehrhardt, C-B Schönlieb
– SIAM Journal on Imaging Sciences
(2021)
14,
814
POINT OF CARE IMAGE ANALYSIS FOR COVID-19
D Yaron, D Keidar, E Goldstein, Y Shachar, A Blass, O Frank, N Schipper, N Shabshin, A Grubstein, D Suhami, NR Bogot, CS Weiss, E Sela, AA Dror, M Vaturi, F Mento, E Torri, R Inchingolo, A Smargiassi, G Soldati, T Perrone, L Demi, M Galun, S Bagon, YM Elyada, YC Eldar
– 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
(2021)
00,
8153
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
P Sellars, AI Aviles-Rivero, C-B Schönlieb
– IEEE Transactions on Neural Networks and Learning Systems 2022
(2021)
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation.
H Peng, AI Aviles-Rivero, C-B Schönlieb
– CoRR
(2021)
abs/2106.03755,
72
Deep learning as optimal control problems ⁎ ⁎ ⁎ MB acknowledges support from the Leverhulme Trust Early Career Fellowship ECF-2016-611 ‘Learning from mistakes: a supervised feedback-loop for imaging applications’. CBS acknowledges support from the Leverhulme Trust project on Breaking the non-convexity barrier, the Philip Leverhulme Prize, the EPSRC grant No. EP/M00483X/1, the EPSRC Centre No. EP/N014588/1, the European Union Horizon 2020 research and innovation programmes under the Marie Skodowska-Curie grant agreement No. 777826 No-MADS and No. 691070 CHiPS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Quadro P6000 and a Titan Xp GPU used for this research. EC and BO thank the SPIRIT project (No. 231632) under the Research Council of Norway FRIPRO funding scheme. This work was supported by EPSRC grant No. EP/K032208/1.
M Benning, E Celledoni, MJ Ehrhardt, B Owren, C-B Schönlieb
– IFAC-PapersOnLine
(2021)
54,
620
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
Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems
S Mukherjee, O Öktem, C-B Schönlieb
– Lecture Notes in Computer Science
(2021)
12679,
540
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
– Nature biomedical engineering
(2021)
5,
509
Dynamic spectral residual superpixels
J Zhang, AI Aviles-Rivero, D Heydecker, X Zhuang, R Chan, C-B Schönlieb
– Pattern Recognition
(2021)
112,
107705
Mechanisms Underlying Vascular Endothelial Growth Factor Receptor Inhibition–Induced Hypertension
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
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 Transactions on Image Processing
(2021)
30,
3555
Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While 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
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
C Runkel, C Etmann, M Möller, C-B Schönlieb
(2021)
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
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
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
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
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
(2021)