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  • Currently displaying 1 - 20 of 230 publications
GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays.
AI Aviles-Rivero, P Sellars, C-B Schönlieb, N Papadakis
– Pattern Recognit
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
X Bai, H Wang, L Ma, Y Xu, J Gan, Z Fan, F Yang, K Ma, J Yang, S Bai, C Shu, X Zou, R Huang, C Zhang, X Liu, D Tu, C Xu, W Zhang, X Wang, A Chen, Y Zeng, D Yang, M-W Wang, N Holalkere, NJ Halin, IR Kamel, J Wu, X Peng, X Wang, J Shao, P Mongkolwat, J Zhang, W Liu, M Roberts, Z Teng, L Beer, LE Sanchez, E Sala, D Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C-B Schönlieb, T Xia
– ArXiv
Enhancing the spatial resolution of hyperpolarized carbon-13 MRI of human brain metabolism using structure guidance.
MJ Ehrhardt, FA Gallagher, MA McLean, C-B Schönlieb
– Magnetic resonance in medicine
Duality-based Higher-order Non-smooth Optimization on Manifolds
W Diepeveen, J Lellmann
– SIAM Journal on Imaging Sciences
Machine learning for workflow applications in screening mammography: systematic review and meta-analysis
SE Hickman, R Woitek, EPV Le, YR Im, C Mouritsen Luxhøj, AI Aviles-Rivero, GC Baxter, JW MacKay, FJ Gilbert
– Radiology
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy.
M Yeung, E Sala, C-B Schönlieb, L Rundo
– Computers in biology and medicine
BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification
C Li, Y Wei, X Chen, CB Schönlieb
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Task adapted reconstruction for inverse problems
J Adler, S Lunz, O Verdier, C-B Schönlieb, O Öktem
– Inverse Problems
Learning optical flow for fast MRI reconstruction
T Schmoderer, AI Aviles-Rivero, V Corona, N Debroux, CB Schönlieb
– Inverse Problems
Radiological tumor classification across imaging modality and histology.
J Wu, C Li, M Gensheimer, S Padda, F Kato, H Shirato, Y Wei, C-B Schönlieb, SJ Price, D Jaffray, J Heymach, JW Neal, BW Loo, H Wakelee, M Diehn, R Li
– Nature Machine Intelligence
Equilibria of an anisotropic nonlocal interaction equation: Analysis and numerics
JA Carrillo, B During, LM Kreusser, CB Schonlieb
– Discrete and Continuous Dynamical Systems
A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images (vol 5, pg 509, 2021)
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
A Geometric Integration Approach to Nonsmooth, Nonconvex Optimisation
ES Riis, MJ Ehrhardt, GRW Quispel, CB Schönlieb
– Foundations of Computational Mathematics
Equivariant neural networks for inverse problems
E Celledoni, MJ Ehrhardt, C Etmann, B Owren, C-B Schönlieb, F Sherry
– Inverse problems
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
– Comput Methods Programs Biomed
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
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation.
H Peng, AI Aviles-Rivero, C-B Schönlieb
– arXiv
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, CB Schonlieb
– IFAC-PapersOnLine
Structure-preserving deep learning
E Celledoni, MJ Ehrhardt, C Etmann, RI Mclachlan, B Owren, CB Schonlieb, F Sherry
– European Journal of Applied Mathematics
Machine churning
M Roberts