| 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 | 848 | 2021 |
| On the Connection Between Adversarial Robustness and Saliency Map Interpretability C Etmann, S Lunz, P Maass, CB Schönlieb International Conference on Machine Learning 2019, 2019 | 160 | 2019 |
| Deep learning for tumor classification in imaging mass spectrometry J Behrmann, C Etmann, T Boskamp, R Casadonte, J Kriegsmann, P Maaβ Bioinformatics 34 (7), 1215-1223, 2018 | 130 | 2018 |
| Conditional image generation with score-based diffusion models G Batzolis, J Stanczuk, CB Schönlieb, C Etmann arXiv preprint arXiv:2111.13606, 2021 | 99 | 2021 |
| Wasserstein GANs work because they fail (to approximate the Wasserstein distance) J Stanczuk, C Etmann, LM Kreusser, CB Schönlieb arXiv preprint arXiv:2103.01678, 2021 | 46 | 2021 |
| Structure preserving deep learning E Celledoni, MJ Ehrhardt, C Etmann, RI McLachlan, B Owren, ... European Journal of Applied Mathematics, 2021 | 44 | 2021 |
| Non-uniform diffusion models G Batzolis, J Stanczuk, CB Schönlieb, C Etmann arXiv preprint arXiv:2207.09786, 2022 | 40 | 2022 |
| iunets: Fully invertible u-nets with learnable up-and downsampling C Etmann, R Ke, CB Schönlieb arXiv preprint arXiv:2005.05220, 2020 | 34* | 2020 |
| Equivariant neural networks for inverse problems E Celledoni, MJ Ehrhardt, C Etmann, B Owren, CB Schönlieb, F Sherry Inverse Problems 37 (8), 085006, 2021 | 24 | 2021 |
| AIX-COVNET M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... Common pitfalls and recommendations for using machine learning to detect and …, 2021 | 18 | 2021 |
| A closer look at double backpropagation C Etmann arXiv preprint arXiv:1906.06637, 2019 | 12 | 2019 |
| Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID‑19 using chest radiographs and CT scans. Nat Mach Intell 3 (3): 199–217 M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... | 11 | 2021 |
| Invertible learned primal-dual J Rudzusika, B Bajic, O Öktem, CB Schönlieb, C Etmann NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021 | 9 | 2021 |
| AIX-COVNET, JHF Rudd, E. Sala & C.-B. Schönlieb,“Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs … M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... | 8 | |
| Deep learning-based segmentation of multisite disease in ovarian cancer T Buddenkotte, L Rundo, R Woitek, L Escudero Sanchez, L Beer, ... European radiology experimental 7 (1), 77, 2023 | 4 | 2023 |
| INSIDEnet: Interpretable nonexpansive data‐efficient network for denoising in grating interferometry breast CT S van Gogh, Z Wang, M Rawlik, C Etmann, S Mukherjee, CB Schönlieb, ... Medical physics 49 (6), 3729-3748, 2022 | 4 | 2022 |
| AIX-COVNET, James HF Rudd, Evis Sala, and Carola-Bibiane Schönlieb. Common pitfalls and recommendations for using machine learning to detect and prognosticate for covid-19 … M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... Nature Machine Intelligence 3 (199-217), 1-5, 2021 | 4 | 2021 |
| CAFLOW: conditional autoregressive flows G Batzolis, M Carioni, C Etmann, S Afyouni, Z Kourtzi, CB Schönlieb arXiv preprint arXiv:2106.02531, 2021 | 2 | 2021 |
| Double Backpropagation with Applications to Robustness and Saliency Map Interpretability C Etmann Universität Bremen, 2019 | 2 | 2019 |
| Deep relevance regularization: Interpretable and robust tumor typing of imaging mass spectrometry data C Etmann, M Schmidt, J Behrmann, T Boskamp, L Hauberg-Lotte, A Peter, ... arXiv preprint arXiv:1912.05459, 2019 | 2 | 2019 |
