| 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 | 1144 | 2021 |
| Solving inverse problems using data-driven models S Arridge, P Maass, O Öktem, CB Schönlieb Acta Numerica 28, 1-174, 2019 | 889 | 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 | 733 | 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 | 388 | 2017 |
| A combined first and second order variational approach for image reconstruction K Papafitsoros, CB Schönlieb Journal of mathematical imaging and vision 48 (2), 308-338, 2014 | 340 | 2014 |
| Adversarial regularizers in inverse problems S Lunz, O Öktem, CB Schönlieb Advances in neural information processing systems 31, 2018 | 316 | 2018 |
| Conditional image generation with score-based diffusion models G Batzolis, J Stanczuk, CB Schönlieb, C Etmann arXiv preprint arXiv:2111.13606, 2021 | 290 | 2021 |
| Can physics-informed neural networks beat the finite element method? TG Grossmann, UJ Komorowska, J Latz, CB Schönlieb IMA Journal of Applied Mathematics 89 (1), 143-174, 2024 | 260 | 2024 |
| 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 | 238 | 2018 |
| 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 | 216 | 2021 |
| 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 | 214 | 2009 |
| On the connection between adversarial robustness and saliency map interpretability C Etmann, S Lunz, P Maass, CB Schönlieb arXiv preprint arXiv:1905.04172, 2019 | 200 | 2019 |
| 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 | 190 | 2021 |
| Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions Y Nan, J Del Ser, S Walsh, C Schönlieb, M Roberts, I Selby, K Howard, ... Information Fusion 82, 99-122, 2022 | 178 | 2022 |
| Unconditionally stable schemes for higher order inpainting A Bertozzi, CB Schönlieb Communications in Mathematical Sciences 9 (2), 413-457, 2011 | 163 | 2011 |
| AI-based reconstruction for fast MRI—A systematic review and meta-analysis Y Chen, CB Schönlieb, P Liò, T Leiner, PL Dragotti, G Wang, D Rueckert, ... Proceedings of the IEEE 110 (2), 224-245, 2022 | 160 | 2022 |
| Partial differential equation methods for image inpainting CB Schönlieb Cambridge University Press, 2015 | 151 | 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 | 149 | 2017 |
| Variational depth from focus reconstruction M Moeller, M Benning, C Schönlieb, D Cremers IEEE Transactions on Image Processing 24 (12), 5369-5378, 2015 | 145 | 2015 |
| Diffmic: Dual-guidance diffusion network for medical image classification Y Yang, H Fu, AI Aviles-Rivero, CB Schönlieb, L Zhu International conference on medical image computing and computer-assisted …, 2023 | 141 | 2023 |
Angelica I. Aviles-RiveroYau Mathematical Sciences Center, Tsinghua UniversityVerified email at tsinghua.edu.cn
