| 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 | 910 | 2021 |
| Solving inverse problems using data-driven models S Arridge, P Maass, O Öktem, CB Schönlieb Acta Numerica 28, 1-174, 2019 | 651 | 2019 |
| 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 | 352 | 2022 |
| 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 | 352 | 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 | 309 | 2014 |
| Adversarial regularizers in inverse problems S Lunz, O Öktem, CB Schönlieb Advances in neural information processing systems 31, 2018 | 258 | 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 | 209 | 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 | 189 | 2018 |
| On the connection between adversarial robustness and saliency map interpretability C Etmann, S Lunz, P Maass, CB Schönlieb arXiv preprint arXiv:1905.04172, 2019 | 166 | 2019 |
| 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 | 156 | 2021 |
| Unconditionally stable schemes for higher order inpainting A Bertozzi, CB Schönlieb Communications in Mathematical Sciences 9 (2), 413-457, 2011 | 151 | 2011 |
| Partial differential equation methods for image inpainting CB Schönlieb Cambridge University Press, 2015 | 138 | 2015 |
| Conditional image generation with score-based diffusion models G Batzolis, J Stanczuk, CB Schönlieb, C Etmann arXiv preprint arXiv:2111.13606, 2021 | 134 | 2021 |
| Variational depth from focus reconstruction M Moeller, M Benning, C Schönlieb, D Cremers IEEE Transactions on Image Processing 24 (12), 5369-5378, 2015 | 129 | 2015 |
| 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 | 128 | 2017 |
| Imaging with Kantorovich--Rubinstein Discrepancy J Lellmann, DA Lorenz, C Schonlieb, T Valkonen SIAM Journal on Imaging Sciences 7 (4), 2833-2859, 2014 | 124 | 2014 |
| Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy M Yeung, E Sala, CB Schönlieb, L Rundo Computers in biology and medicine 137, 104815, 2021 | 117 | 2021 |
| Image denoising: learning the noise model via nonsmooth PDE-constrained optimization. JC De los Reyes, CB Schönlieb Inverse Problems & Imaging 7 (4), 2013 | 115 | 2013 |
| Bilevel approaches for learning of variational imaging models L Calatroni, C Chung, JC De Los Reyes, CB Schönlieb, T Valkonen Variational Methods: In Imaging and Geometric Control 18 (252), 2, 2017 | 110 | 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 | 102 | 2020 |
Angelica I. Aviles-RiveroUniversity of Cambridge在 cam.ac.uk 的电子邮件经过验证
