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Bild Dr. Daniel Otero Baguer

Dr. Daniel Otero Baguer

Research Assistant WG Industrial Mathematics

Room: MZH 2060
Email: otero@math.uni-bremen.de
Phone: (0421) 218-63816

Projects

  1. DIAMANT - Digital Image Analysis and Imaging Mass Spectrometry to Differentiate Non-small Cell Lung Cancer (01.01.2020 - 31.12.2022)
  2. SFB 1232: Farbige Zustände - TP P02: Heuristische, statistische und analytische Versuchsplanung (01.07.2016 - 30.06.2020)

Courses (Selection)complete list

  1. Oberseminar Mathematical Parameter Identification (RTG-Seminar) (Sommersemester 2021)

Theses (Selection)complete list

  1. Theorie und Anwendung des Analytic-Deep-Prior-Ansatzes (Clemens Arndt)
  2. Invertible U-Nets for Memory-Efficient Backpropagation (Nick Heilenkötter)

Publications (Selection)complete list

  1. 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: 10.3390/jimaging7040071

  2. 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

  3. D. Otero Baguer.
    Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging. (submitted).
    Dissertationsschrift, Universität Bremen, 2020.
  4. 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

  5. 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, 62(3):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