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.

DOI: https://doi.org/10.3390/jimaging7040071

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.

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

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"