Alexander Denker
Alexander Denker
Wissenschaftlicher Mitarbeiter
Doktorand Graduiertenkolleg π3
Team Deep Learning und Inverse Probleme
Bibliothekstraße 5
28359 Bremen
Raum: MZH 2410
Telefon: +49 421 218-63897
E-Mail: adenkerprotect me ?!uni-bremenprotect me ?!.de
Forschungsgebiete
- Deep Learning
- Inverse Probleme
- Computertomographie
Projekte
- Graduiertenkolleg π³ - Parameter Identification – Analysis, Algorithms, Applications
- MALDISTAR - Studie zur Qualitätsbewertung, Standardisierung und Reproduzierbarkeit von Daten der bildgebenden MALDI-Massenspektrometrie
Zeitschriftenartikel
F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
Inverse Problems, 39(6), 2023.
online unter: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta
C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
Applied Mathematics for Modern Challenges, 1(2), 2023.
DOI: 10.3934/ammc.2023007
A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
Score-Based Generative Models for PET Image Reconstruction.
Erscheint in Machine Learning for Biomedical Imaging
online unter: https://arxiv.org/abs/2308.14190
C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, G. Rigaud.
In Focus - hybrid deep learning approaches to the HDC2021 challenge.
Inverse Problems and Imaging, 2022.
DOI: 10.3934/ipi.2022061
R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maaß, B. Jin.
An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction.
IEEE Transactions on Computational Imaging, 8:1210-1222, 2022.
DOI: 10.1109/TCI.2022.3233188
A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
Conditional Invertible Neural Networks for Medical Imaging .
MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021.
DOI: 10.3390/jimaging7110243
J. Leuschner, M. Schmidt, P. Ganguly, V. Andriiashen, S. Coban, A. Denker, D. Bauer, A. Hadjifaradji, K. Batenburg, B. Maass, M. von Eijnatten.
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
MDPI Journal of Imaging, 7(3), 44 S., 2021.
DOI: 10.3390/jimaging7030044
online unter: https://www.mdpi.com/2313-433X/7/3/44
C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
Invertible residual networks in the context of regularization theory for linear inverse problems.
Inverse Problems, 39(12), 2023.
DOI: 10.1088/1361-6420/ad0660
Preprints
Tagungsbeiträge
M. Schmidt, A. Denker, J. Leuschner.
The Deep Capsule Prior - advantages through complexity.
GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.
DOI: 10.1002/pamm.202100166
A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.
online unter: https://invertibleworkshop.github.io/accepted_papers/index.html
2020 | Masterarbeit am ZeTeM, "Application of Neural Networks For Solving Inverse Problems." |
2017 | Bacherlorarbeit am ZeTeM, "Deep-Learning-Konzepte zur Optimierung von ISTA-Verfahren." |