Publikationen von Dr. Maximilian Schmidt

Buchkapitel (2)

1. P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
   Mathematische Architekturen für Neuronale Netze.
   Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.

   DOI: 10.1007/s00591-022-00325-y

2. J. Behrmann, M. Schmidt, J. Wildner, P. Maaß, S. Schmale.
   Purity Assessment of Pellets Using Deep Learning.
   German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.), Mathematics in Industry, S. 29-34, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7_6

Zeitschriftenartikel (8)

1. 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

2. 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

3. 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

4. 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

5. J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
   LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
   Scientific Data, 8(109), 2021.

   DOI: 10.1038/s41597-021-00893-z

6. 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

7. D. Otero Baguer, J. Leuschner, M. Schmidt.
   Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
   Inverse Problems, 36(9), IOPscience, 2020.

   DOI: 10.1088/1361-6420/aba415

8. J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maaß.
   Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
   Bioinformatics, bty909 , 2018.

   DOI: 10.1093/bioinformatics/bty909

Preprints (3)

1. T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
   Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
   Zur Veröffentlichung eingereicht.

   online unter: https://aps.arxiv.org/abs/2307.10474

2. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2302.02181

3. C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maaß.
   Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/1912.05459

Qualifikationsarbeiten (1)

1. M. Schmidt.
   Hybrid Deep Learning: How Combining Data-Driven and Model-Based Approaches Solves Inverse Problems in Computed Tomography and Beyond.
   Dissertationsschrift, Universität Bremen, 2022.

   DOI: 10.26092/elib/1941

Tagungsbeiträge (3)

1. M. Schmidt.
   Around the clock - capsule networks and image transformations.
   PAMM.
   Proceedings in Applied Mathematics and Mechanics, 20(1):e202000179, 2021.

   DOI: 10.1002/pamm.202000179
   online unter: https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202000179

2. M. Schmidt, A. Denker, J. Leuschner.
   The Deep Capsule Prior - advantages through complexity.
   GAMM 91st 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

3. 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