Dr. Daniel Otero Baguer
Research Assistant WG Industrial MathematicsProjects
- DIAMANT - Digital Image Analysis and Imaging Mass Spectrometry to Differentiate Non-small Cell Lung Cancer (01.01.2020 - 31.12.2022)
- SFB 1232: Farbige Zustände - TP P02: Heuristische, statistische und analytische Versuchsplanung (01.07.2016 - 30.06.2020)
Courses (Selection)
- Oberseminar Mathematical Parameter Identification (RTG-Seminar) (Sommersemester 2022)
- Oberseminar Mathematical Parameter Identification (RTG-Seminar) (Sommersemester 2021)
Theses (Selection)
- Interpretability and Explainability of Neural Networks applied in Digital Pathology (Rudolf Herdt)
- Theorie und Anwendung des Analytic-Deep-Prior-Ansatzes (Clemens Arndt)
- Invertible U-Nets for Memory-Efficient Backpropagation (Nick Heilenkötter)
Publications (Selection)
- R. Herdt, M. Schmidt, D. Otero Baguer, P. Maaß.
Smooth Deep Saliency.
Zur Veröffentlichung eingereicht.online at: https://arxiv.org/abs/2404.02282
- R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
How GAN Generators can Invert Networks in Real-Time.
The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023.
PMLR, 222:422-437, 2024. - 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. - 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
- 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.