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Christian Etmann
Christian Etmann
Senior Research Scientist, Deep Render
Verified email at deeprender.ai
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Cited by
Cited by
Year
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
8362021
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
1592019
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
1302018
Conditional image generation with score-based diffusion models
G Batzolis, J Stanczuk, CB Schönlieb, C Etmann
arXiv preprint arXiv:2111.13606, 2021
942021
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
462021
Structure preserving deep learning
E Celledoni, MJ Ehrhardt, C Etmann, RI McLachlan, B Owren, ...
European Journal of Applied Mathematics, 2021
442021
Non-uniform diffusion models
G Batzolis, J Stanczuk, CB Schönlieb, C Etmann
arXiv preprint arXiv:2207.09786, 2022
392022
iunets: Fully invertible u-nets with learnable up-and downsampling
C Etmann, R Ke, CB Schönlieb
arXiv preprint arXiv:2005.05220, 2020
33*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
242021
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
182021
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, ...
112021
A closer look at double backpropagation
C Etmann
arXiv preprint arXiv:1906.06637, 2019
112019
Invertible learned primal-dual
J Rudzusika, B Bajic, O Öktem, CB Schönlieb, C Etmann
NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021
82021
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
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
42022
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
42021
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
32023
CAFLOW: conditional autoregressive flows
G Batzolis, M Carioni, C Etmann, S Afyouni, Z Kourtzi, CB Schönlieb
arXiv preprint arXiv:2106.02531, 2021
22021
Double Backpropagation with Applications to Robustness and Saliency Map Interpretability
C Etmann
Universität Bremen, 2019
22019
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
22019
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