Dr. Daniel Otero Baguer

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 2022)
2. Oberseminar Mathematical Parameter Identification (RTG-Seminar) (Sommersemester 2021)

Theses (Selection)complete list

1. Interpretability and Explainability of Neural Networks applied in Digital Pathology (Rudolf Herdt)
2. Theorie und Anwendung des Analytic-Deep-Prior-Ansatzes (Clemens Arndt)
3. Invertible U-Nets for Memory-Efficient Backpropagation (Nick Heilenkötter)

Publications (Selection)complete list

1. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2302.02181

2. 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

3. 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

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