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. P. Jansen, J. Le Clerc Arrastia, D. Otero Baguer, M. Schmidt, J. Landsberg, J. Wenzel, M. Emberger, D. Schadendorf, E. Hadaschik, P. Maaß, K. G. Griewank.
   Deep learning based histological classification of adnex tumors.
   European Journal of Cancer, 113431 196, 2024.

   DOI: 10.1016/j.ejca.2023.113431

2. 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

3. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   How GAN Generators can Inverta Networks in Real-Time.
   The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023.
   PMLR, 222:422-437, 2023.

   online at: https://proceedings.mlr.press/v222/herdt24a.html

4. 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

5. 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