Dr. Daniel Otero Baguer
Research Assistant WG Industrial MathematicsProjects
- DIAMANT - Digital Image Analysis and Imaging Mass Spectrometry to Differentiate Non-small Cell Lung Cancer (01.01.2020 - 31.12.2022)
- SFB 1232: Farbige Zustände - TP P02: Heuristische, statistische und analytische Versuchsplanung (01.07.2016 - 30.06.2020)
Theses (Selection)
- Invertible U-Nets for Memory-Efficient Backpropagation (Nick Heilenkötter)
Publications (Selection)
- D. Otero Baguer, J. Leuschner, M. Schmidt.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
Inverse Problems, 36(9), IOPscience, 2020. - D. Otero Baguer.
Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging. (submitted).
Dissertationsschrift, Universität Bremen, 2020. - 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 (Eds.), pp. 113-122, 2020. - 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, :456-470, Springer Verlag, 2020.DOI: 10.1007/s10851-019-00923-x
online at: http://link.springer.com/article/10.1007/s10851-019-00923-x - J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.
Zur Veröffentlichung eingereicht.online at: arXiv:1910.01113