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Dr. Daniel Otero Baguer

Foto Daniel Otero Baguer

Dr. Daniel Otero Baguer

Research associate

Deep Learning and Digital Pathology

Bibliothekstraße 5
28359 Bremen

Office: MZH 2060
Phone: +49 421 218-63816
E-Mail: oteroprotect me ?!math.uni-bremenprotect me ?!.de

Research areas

  • Inverse Problems
  • Machine Learning / Deep Learning
  • Image and signal processing in the life sciences
  • Computational Engineering
  • Digital Pathology


Coordinator of the Research Training Group π³ - Parameter Identification – Analysis, Algorithms, Applications

  • DIAMANT - Digital image analysis and imaging mass spectrometry for differentiation of non-small cell lung cancer
  • SPAplus - Small Data problems in digital pathology


  • Invertible U-Nets for Memory-Efficient Backpropagation, Bachelorarbeit, Nick Heilenkötter, 2020


Journal articles (4)

J. Le Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner , J. Schaller , P. Maaß.
Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma.
MDPI Journal of Imaging, 71 7(4), Meisenbach Verlag, Bamberg, 2021.


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

D. Otero Baguer, J. Leuschner, M. Schmidt.
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
Inverse Problems, 36(9), IOPscience, 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

Conference proceedings (3)

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.

DOI: 10.1007/978-3-030-61598-7_11

T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.

DOI: 10.1063/1.5112634

D. Otero Baguer, I. Piotrowska, P. Maaß.
Inverse Problems in designing new structural materials.
7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.

DOI: 10.1007/978-3-030-55240-4_8




since 2020Postdoc at the Center for Industrial Mathematics, University of Bremen
2020Dr.-rer.nat., Center for Industrial Mathematics, University of Bremen, "Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging"
2017 - 2020PhD. student at the Center for Industrial Mathematics, University of Bremen
2010 - 2015Diplom Computer Science, Universidad de la Habana, Cuba, "Applications of Wavelets transforms in the processing, analysis and classification of bioacoustics signals"