Publikationen der AG Technomathematik

Zeitschriftenartikel (12)

1. O. Klein, F. Fogt, S. Hollerbach, G. Nebrich, T. Boskamp, A. Wellmann.
   Classification of Inflammatory Bowel Disease from Formalin‐Fixed, Paraffin‐Embedded Tissue Biopsies via Imaging Mass Spectrometry.
   Proteomics - Clinical Applications, 190131 , Wiley, 2020.

   DOI: 10.1002/prca.201900131

2. 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

3. M. Beckmann, P. Maaß, J. Nickel.
   Error analysis for filtered back projection reconstructions in Besov spaces.
   Inverse Problems, 37 014002 37(1), IOPscience, 2020.
4. T. Kluth, C. Bathke, M. Jiang, P. Maaß.
   Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
   Inverse Problems, 36(12), 2020.

   DOI: https://doi.org/10.1088/1361-6420/abc2fe

5. T. Kluth, B. Jin.
   L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
   International Journal on Magnetic Particle Imaging, , 2020.

   DOI: 10.18416/IJMPI.2020.2012001
   online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/146

6. T. H. Nguyen, D. Nho Hào, P. Maaß, L. Colombi Ciacchi.
   Mathematical aspects of catalyst positioning in lithium/air batteries.
   Inverse Problems, 36(4), 2020.

   DOI: 10.1088/1361-6420/ab47e6

7. F. Lieb, T. Boskamp, H. Stark.
   Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers.
   Journal of Proteomics, 103852 225, Elsevier, 2020.

   DOI: 10.1016/j.jprot.2020.103852

8. T. Kluth.
   Recent developments on system function/matrix representation, hybrid simulation techniques, and magnetic actuation.
   International Journal on Magnetic Particle Imaging, 6(1), 2020.

   DOI: https://journal.iwmpi.org/index.php/iwmpi/article/view/327

9. G. Rigaud, B. Hahn.
   Reconstruction Algorithm For 3D Compton Scattering Imaging With Incomplete Data.
   Erscheint in Inverse Problems in Science and Engineering
10. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
    Regularization by architecture: A deep prior approach for inverse problems.
    Journal of Mathematical Imaging and Vision, 62(3):456-470, Springer Verlag, 2020.

    DOI: 10.1007/s10851-019-00923-x
    online unter: http://link.springer.com/article/10.1007/s10851-019-00923-x

11. T. Kluth, H. Albers.
    Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging.
    Erscheint in Oberwolfach Reports
12. T. Boskamp, D. Lachmund, R. Casadonte, L. Hauberg-Lotte, J. H. Kobarg, J. Kriegsmann, P. Maaß.
    Using the chemical noise background in MALDI mass spectrometry imaging for mass alignment and calibration.
    Analytical Chemistry, 92(1):1301-1308, 2020.

    DOI: 10.1021/acs.analchem.9b04473
    online unter: https://doi.org/10.1021/acs.analchem.9b04473

Tagungsbeiträge (6)

1. S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
   A Deep Prior Approach to Magnetic Particle Imaging.
   Machine Learning for Medical Image Reconstruction 2020.
   Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Hrsg.), S. 113-122, 2020.

   DOI: 10.1007/978-3-030-61598-7_11

2. 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

3. M. Möddel, F. Griese, T. Kluth, T. Knopp.
   Estimating orientation using multi-contrast MPI.
   10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
   International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):3 pages, Infinite Science Publishing, 2020.

   DOI: 10.18416/IJMPI.2020.2009023

4. F. Tramer, J. Behrmann, N. Carlini, N. Papernot, J. Jacobsen.
   Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.
   International Conference on Machine Learning (ICML), 12.07 - 18.07.2020, Wien, Österreich.
   

   online unter: https://arxiv.org/abs/2002.04599

5. H. Albers, T. Kluth, T. Knopp.
   MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
   10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
   International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):3 pages, Infinite Science Publishing, 2020.

   DOI: 10.18416/IJMPI.2020.2009020

6. T. Kluth, P. Szwargulski, T. Knopp.
   Towards accurate modeling of the multidimensional MPI physics.
   10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
   International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):2 pages, Infinite Science Publishing, 2020.

   DOI: 10.18416/IJMPI.2020.2009004

Qualifikationsarbeiten (4)

1. C. Etmann.
   Double Backpropagation with Applications to Robustness and Saliency Map Interpretability.
   Dissertationsschrift, Universität Bremen, 2020.
2. D. Otero Baguer.
   Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging. (submitted).
   Dissertationsschrift, Universität Bremen, 2020.
3. S. Dittmer.
   On deep learning applied to inverse problems - A chicken-and-egg problem.
   Dissertationsschrift, Universität Bremen, 2020.
4. C. Brandt.
   Recurrence Quantification Compared to Fourier Analysis for Ultrasonic Non-Destructive Testing of Fibre Reinforced Polymers.
   Dissertationsschrift, Universität Bremen, 2020.

Preprints (4)

1. L. Kuger, G. Rigaud.
   Joint fan-beam CT and Compton scattering tomography: analysis and image reconstruction.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2008.06699

2. S. . Mukherjee, S. Dittmer, Z. . Shumaylov, S. Lunz, O. Öktem, C. Schönlieb.
   Learned convex regularizers for inverse problems.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2008.02839

3. I. Piotrowska-Kurczewski, G. Sfakianaki.
   Tikhonov functionals with a tolerance measure introduced in the regularization.
   Zur Veröffentlichung eingereicht.

   online unter: http://arxiv.org/abs/2007.06431

4. J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
   Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2006.09347