Publications of Dr. Daniel Otero Baguer

Articles (4)

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. 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

Preprints (1)

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

PhD/Habilitation Thesis (1)

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

Proceedings (3)

1. 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

2. T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
   Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
   22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
   AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.

   DOI: 10.1063/1.5112634

3. D. Otero Baguer, P. Maaß.
   Inverse Problems in designing new structural materials.
   7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.
   

   DOI: 10.1007/978-3-030-55240-4_8