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Currently displaying 141 - 160 of 310 publications

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

(doi: 10.1137/20m1357500)

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)

(link to publication)

HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation

H Peng, AI Aviles-Rivero, C-B Schönlieb

– 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

(2021)

abs/2106.03755,

(doi: 10.1109/WACV51458.2022.00015)

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

(doi: 10.1016/j.ifacol.2021.06.124)

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

(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

– Nature Biomedical Engineering

(2021)

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

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)

Deep learning based segmentation of brain tissue from diffusion MRI

F Zhang, A Breger, KIK Cho, L Ning, C-F Westin, LJ O'Donnell, O Pasternak

– Neuroimage

(2021)

233,

117934

(doi: 10.1016/j.neuroimage.2021.117934)

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

(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)

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)

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)