| Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications J Leuschner, M Schmidt, PS Ganguly, V Andriiashen, SB Coban, ... Journal of Imaging 7 (3), 44, 2021 | 44 | 2021 |
| Conditional invertible neural networks for medical imaging A Denker, M Schmidt, J Leuschner, P Maass Journal of Imaging 7 (11), 243, 2021 | 36 | 2021 |
| An educated warm start for deep image prior-based micro CT reconstruction R Barbano, J Leuschner, M Schmidt, A Denker, A Hauptmann, P Maass, ... IEEE Transactions on Computational Imaging 8, 1210-1222, 2022 | 19* | 2022 |
| PatchNR: learning from very few images by patch normalizing flow regularization F Altekrüger, A Denker, P Hagemann, J Hertrich, P Maass, G Steidl Inverse Problems 39 (6), 064006, 2023 | 18* | 2023 |
| Conditional normalizing flows for low-dose computed tomography image reconstruction A Denker, M Schmidt, J Leuschner, P Maass, J Behrmann arXiv preprint arXiv:2006.06270, 2020 | 17 | 2020 |
| Invertible residual networks in the context of regularization theory for linear inverse problems C Arndt, A Denker, S Dittmer, N Heilenkötter, M Iske, T Kluth, P Maass, ... Inverse Problems 39 (12), 125018, 2023 | 6 | 2023 |
| Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems R Barbano, A Denker, H Chung, TH Roh, S Arrdige, P Maass, B Jin, ... arXiv preprint arXiv:2308.14409, 2023 | 4 | 2023 |
| Score-based generative models for PET image reconstruction IRD Singh, A Denker, R Barbano, Ž Kereta, B Jin, K Thielemans, P Maass, ... arXiv preprint arXiv:2308.14190, 2023 | 2 | 2023 |
| Investigating intensity normalisation for pet reconstruction with supervised deep learning I Singh, A Denker, B Jin, K Thielemans, S Arridge IEEE, 2024 | 1 | 2024 |
| MR-blob: Coordinate-Transformed Blobs for Parallel MRI Reconstruction Z Kereta, A Denker, R Barbano, B Jin, K Thielemans, S Arrdige, I Singh | | 2024 |
| Model-based deep learning approaches to the Helsinki Tomography Challenge 2022 C Arndt, A Denker, S Dittmer, J Leuschner, J Nickel, M Schmidt Applied Mathematics for Modern Challenges 1 (2), 87-104, 2023 | | 2023 |
| In Focus-hybrid deep learning approaches to the HDC2021 challenge. C Arndt, A Denker, J Nickel, J Leuschner, M Schmidt, G Rigaud Inverse Problems & Imaging 17 (5), 2023 | | 2023 |
| The Deep Capsule Prior–advantages through complexity? M Schmidt, A Denker, J Leuschner PAMM 21 (1), e202100166, 2021 | | 2021 |
| Feature reduction for machine learning on molecular features: The GeneScore A Denker, A Steshina, T Grooss, F Ueckert, S Nürnberg arXiv preprint arXiv:2101.05546, 2021 | | 2021 |
| Bringing advanced analytics experts to the data: Report of an interdisciplinary machine learning workshop S Stahl-Toyota, K Glocker, AP Perez, A Knurr, A Denker, F Ückert Cancer Research 80 (16_Supplement), 2087-2087, 2020 | | 2020 |
