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Bild Dr. Daniel Otero Baguer

Dr. Daniel Otero Baguer

Research Assistant WG Industrial Mathematics

Room: MZH 2130
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, J. Leuschner, M. Schmidt.
    Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
    Inverse Problems, 36(9), IOPscience, 2020.

    DOI: 10.1088/1361-6420/aba415

  4. D. Otero Baguer.
    Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging. (submitted).
    Dissertationsschrift, Universität Bremen, 2020.
  5. 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