Dr. Johannes Leuschner

Ehemaliger Mitarbeiter der WG Industrial Mathematics, Research Training Group π³


Information: Email ends with @uni-bremen.de

Research Areas

• Computed Tomography
• Deep Learning
• Inverse Problems

Courses (Selection)complete list

1. Computerpraktikum (Wintersemester 2020/2021)
2. Computerpraktikum (Wintersemester 2019/2020)

Theses (Selection)complete list

1. Modellierung von Geometrieabweichungen bei der Nano-Computertomographie (Tom Lütjen)
2. Using Neural Networks to Denoise CT Images (Rudolf Herdt)

Publications (Selection)complete list

1. M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß.
   SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
   Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.
   

   online at: https://2023.midl.io/papers/p014

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. J. Leuschner.
   Deep Learning for Computed Tomography Reconstruction: Learned Methods, Deep Image Prior, and Uncertaninty Estimation.
   Dissertationsschrift, Universität Bremen, 2023.

   DOI: 10.26092/elib/2704

4. J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
   Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
   Transactions on Machine Learning Research, 12, 2023.

   online at: https://openreview.net/forum?id=FWyabz82fH

5. 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 at: https://aps.arxiv.org/abs/2307.10474

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