Carola-Bibiane Schönlieb

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Angelica I. Aviles-RiveroUniversity of CambridgeVerified email at cam.ac.uk
Martin BenningQueen Mary University of LondonVerified email at qmul.ac.uk
Martin BurgerDeutsches Elektronen-Synchrotron DESY und Universität HamburgVerified email at uni-hamburg.de
Matthias J. EhrhardtMathematical Sciences, University of BathVerified email at bath.ac.uk
Juan Carlos De los ReyesCentro de Modelización Matemática (MODEMAT), Escuela Politécnica Nacional, EcuadorVerified email at epn.edu.ec
David CoomesProfessor of Forest Ecology and Conservation, University of CambridgeVerified email at cam.ac.uk
Xiaohao CaiSchool of Electronics and Computer Science, University of SouthamptonVerified email at soton.ac.uk
Luca CalatroniCNRS, I3S, Sophia-Antipolis, FranceVerified email at i3s.unice.fr
Jingwei LiangShanghai Jiao Tong UniversityVerified email at sjtu.edu.cn
Jan LellmannUniversity of LuebeckVerified email at uni-luebeck.de
Kostas PapafitsorosQueen Mary University of LondonVerified email at qmul.ac.uk
Simone ParisottoSoftware Developer, Datacolor Industrial SrlVerified email at simoneparisotto.com
Juheon LeeStaff Research Scientist, HP LabsVerified email at hp.com
Bertram DüringProfessor of Mathematics, University of WarwickVerified email at warwick.ac.uk
Michael MoellerProfessor for Computer Vision, University of Siegen, GermanyVerified email at uni-siegen.de
Massimo FornasierProfessor of Applied Numerical Analysis, Technical University of MunichVerified email at ma.tum.de
Robert ToveyUniversity College London (UCL)Verified email at ucl.ac.uk
Paul MidgleyProfessor, University of CambridgeVerified email at cam.ac.uk
Daniel CremersTechnical University of MunichVerified email at tum.de
Peter MarkowichKAUSTVerified email at kaust.edu.sa

Carola-Bibiane Schönlieb

DAMTP, University of Cambridge

Verified email at cam.ac.uk

Mathematics Computer Science Medical imaging inverse problems


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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, ... Nature Machine Intelligence 3 (3), 199-217, 2021	604	2021
Solving inverse problems using data-driven models S Arridge, P Maass, O Öktem, CB Schönlieb Acta Numerica 28, 1-174, 2019	444	2019
Learning to diversify deep belief networks for hyperspectral image classification P Zhong, Z Gong, S Li, CB Schönlieb IEEE Transactions on Geoscience and Remote Sensing 55 (6), 3516-3530, 2017	298	2017
A combined first and second order variational approach for image reconstruction K Papafitsoros, CB Schönlieb Journal of mathematical imaging and vision 48, 308-338, 2014	285	2014
Adversarial regularizers in inverse problems S Lunz, O Öktem, CB Schönlieb Advances in neural information processing systems 31, 2018	199	2018
Cahn–Hilliard inpainting and a generalization for grayvalue images M Burger, L He, CB Schönlieb SIAM Journal on Imaging Sciences 2 (4), 1129-1167, 2009	194	2009
Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications A Chambolle, MJ Ehrhardt, P Richtárik, CB Schonlieb SIAM Journal on Optimization 28 (4), 2783-2808, 2018	160	2018
Unconditionally stable schemes for higher order inpainting A Bertozzi, CB Schönlieb Communications in Mathematical Sciences 9 (2), 413-457, 2011	141	2011
Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation M Yeung, E Sala, CB Schönlieb, L Rundo Computerized Medical Imaging and Graphics 95, 102026, 2022	132	2022
On the connection between adversarial robustness and saliency map interpretability C Etmann, S Lunz, P Maass, CB Schönlieb arXiv preprint arXiv:1905.04172, 2019	128	2019
Partial differential equation methods for image inpainting CB Schönlieb Cambridge University Press, 2015	123	2015
Variational depth from focus reconstruction M Moeller, M Benning, C Schönlieb, D Cremers IEEE Transactions on Image Processing 24 (12), 5369-5378, 2015	114	2015
Imaging with Kantorovich--Rubinstein Discrepancy J Lellmann, DA Lorenz, C Schonlieb, T Valkonen SIAM Journal on Imaging Sciences 7 (4), 2833-2859, 2014	110	2014
Bilevel parameter learning for higher-order total variation regularisation models JC De los Reyes, CB Schönlieb, T Valkonen Journal of Mathematical Imaging and Vision 57 (1), 1-25, 2017	106	2017
Image denoising: learning the noise model via nonsmooth PDE-constrained optimization JC De los Reyes, CB Schönlieb Inverse Problems & Imaging 7 (4), 1183, 2013	106	2013
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, ... Nature biomedical engineering 5 (6), 509-521, 2021	89	2021
Bilevel approaches for learning of variational imaging models L Calatroni, C Cao, JC De Los Reyes, CB Schönlieb, T Valkonen Variational Methods: In Imaging and Geometric Control 18 (252), 2, 2017	85	2017
Tuning-free plug-and-play proximal algorithm for inverse imaging problems K Wei, A Aviles-Rivero, J Liang, Y Fu, CB Schönlieb, H Huang International Conference on Machine Learning, 10158-10169, 2020	81	2020
Combined first and second order total variation inpainting using split Bregman K Papafitsoros, CB Schoenlieb, B Sengul Image Processing On Line, 2013	81	2013
Liquid phase blending of metal-organic frameworks L Longley, SM Collins, C Zhou, GJ Smales, SE Norman, NJ Brownbill, ... Nature communications 9 (1), 2135, 2018	71	2018

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