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 (9)

1. P. Jansen, J. Le Clerc Arrastia, D. Otero Baguer, M. Schmidt, J. Landsberg, J. Wenzel, M. Emberger, D. Schadendorf, E. Hadaschik, P. Maaß, K. G. Griewank.
   Deep learning based histological classification of adnex tumors.
   European Journal of Cancer, 113431 196, 2024.

   DOI: 10.1016/j.ejca.2023.113431

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. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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 (4)

1. R. Herdt, M. Schmidt, D. Otero Baguer, P. Maaß.
   Smooth Deep Saliency.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2404.02282

2. 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

3. 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

4. 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 (4)

1. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   How GAN Generators can Invert Networks in Real-Time.
   The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023.
   PMLR, 222:422-437, 2024.

   online unter: https://proceedings.mlr.press/v222/herdt24a.html

2. 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

3. 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

4. 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