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Johannes Leuschner

Johannes Leuschner

Johannes Leuschner

Wissenschaftlicher Mitarbeiter

Doktorand Graduiertenkolleg π3
Team Deep Learning und Inverse Probleme

Bibliothekstraße 5
28359 Bremen

Raum: MZH 2050
Telefon: +49 421 218-63811
E-Mail: jleuschnprotect me ?!uni-bremenprotect me ?!.de

Forschungsgebiete

  • Computertomographie
  • Deep Learning
  • Inverse Probleme

Projekte

  • DELETO - Maschinelles Lernen bei korrelativer MR und Hochdurchsatz-NanoCT

  • Graduiertenkolleg π³ - Parameter Identification – Analysis, Algorithms, Applications

Abschlussarbeiten

  • Using Neural Networks to Denoise CT Images, Bachelorarbeit, Rudolf Herdt, 2020

 

Zeitschriftenartikel

S. Schulze, J. Leuschner, E. King.
Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients.
MDPI Open Access Journals Signals, 2(4):637-661, 2021.
DOI: 10.3390/signals2040039.
Online unter: https://www.mdpi.com/2624-6120/2/4/39

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

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

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

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: https://doi.org/10.1088/1361-6420/aba415

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

R. Barbano, J. Leuschner, M. Schmidt, A. Denker, P. Maaß, B. Jin.
Is Deep Image Prior in Need of a Good Education?
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2111.11926

 

Tagungsbeiträge

M. Schmidt, A. Denker, J. Leuschner.
The Deep Capsule Prior - advantages through complexity.
GAMM 92st 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

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

 

Sonstiges

C. Brandt, M. Hamann, J. Leuschner.
Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
Berichte aus der Technomathematik 19–01, Universität Bremen, 2019.

 

 

 

  • Kurs, Computerpraktikum, 03-M-COM-1,WiSe 2020/2021
  • Computerpraktikum, 03-M-COM-1, WiSe 2019/2020