Dr. Alexander Denker

Theses (Selection)complete list

1. Active learning for semantic segmentation in digital pathology (Jannik Wildner)
2. Adversarial Examples in Deep-Learning-Rekonstruktionen am Beispiel von Computer-Tomographie (Fabian Schönfeld)
3. Fehlererkennung und –segmentierung von Stahlcoils unter Verwendung des Contrastive Learnings (Dennis Hottendorff)

Publications (Selection)complete list

1. 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), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online at: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

2. 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

3. 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 at: https://arxiv.org/abs/2308.14190

4. 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 at: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

5. 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