% This file was created by ZeTeM Website
 
% Maximilian Schmidt




@INBOOK{Maass2022_b8322,
  author = {Peter Maaß and Sören Dittmer and Tobias Kluth and Johannes Leuschner and Maximilian Schmidt},
  chapter = {Mathematische Architekturen für Neuronale Netze},
  title = {Erfolgsformeln – Anwendungen der Mathematik},
  editor = {Matthias Ehrhardt and Michael Günther and Wil Schilders},
  series = {Mathematische Semesterberichte},
  pages = {190-195},
  publisher = {Springer Verlag},
  year = {2022},
  doi = {10.1007/s00591-022-00325-y}
}


@INBOOK{Behrmann2022_da7fc,
  author = {Jens Behrmann and Maximilian Schmidt and Jannik Wildner and Peter Maaß and Sebastian Schmale},
  chapter = {Purity Assessment of Pellets Using Deep Learning},
  title = {German Success Stories in Industrial Mathematics},
  editor = {H.G. Bock and KH. Küfer and Peter Maaß and A. Milde and V. Schulz},
  series = {Mathematics in Industry},
  pages = {29-34},
  publisher = {Springer Verlag},
  year = {2022},
  doi = {10.1007/978-3-030-81455-7_6}
}


@ARTICLE{Jansen2024_77d72,
  author = {Philipp Jansen and Jean Le Clerc Arrastia and Daniel Otero Baguer and Maximilian Schmidt and Jennifer Landsberg and Jörg Wenzel and Michael Emberger and Dirk Schadendorf and Eva Hadaschik and Peter Maaß and Klaus Georg Griewank},
  title = {Deep learning based histological classification of adnex tumors},
  journal = {European Journal of Cancer},
  annote = {113431},
  volume = {196},
  year = {2024},
  doi = {10.1016/j.ejca.2023.113431}
}


@ARTICLE{Arndt2023_c4b93,
  author = {Clemens Arndt and Alexander Denker and Sören Dittmer and Johannes Leuschner and Judith Nickel and Maximilian Schmidt},
  title = {Model-based deep learning approaches to the Helsinki Tomography Challenge 2022},
  journal = {Applied Mathematics for Modern Challenges},
  volume = {1},
  number = {2},
  year = {2023},
  doi = {10.3934/ammc.2023007}
}


@ARTICLE{Barbano2022_4dd3b,
  author = {R. Barbano and Johannes Leuschner and Maximilian Schmidt and Alexander Denker and Andreas Hauptmann and Peter Maaß and Bangti Jin},
  title = {An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction},
  journal = {IEEE Transactions on Computational Imaging},
  volume = {8},
  pages = {1210-1222},
  year = {2022},
  doi = {10.1109/TCI.2022.3233188}
}


@ARTICLE{Arndt2022_916b9,
  author = {Clemens Arndt and Alexander Denker and Judith Nickel and Johannes Leuschner and Maximilian Schmidt and Gael Rigaud},
  title = {In Focus - hybrid deep learning approaches to the HDC2021 challenge},
  journal = {Inverse Problems and Imaging},
  year = {2022},
  doi = {10.3934/ipi.2022061}
}


@ARTICLE{Denker2021_ef45b,
  author = {Alexander Denker and Maximilian Schmidt and Johannes Leuschner and Peter Maaß},
  title = {Conditional Invertible Neural Networks for Medical Imaging },
  journal = {MDPI Journal of Imaging},
  annote = {Inverse Problems and Imaging},
  volume = {7},
  number = {11},
  pages = {243},
  year = {2021},
  doi = {10.3390/jimaging7110243 }
}


@ARTICLE{Leuschner2021_5441e,
  author = {Johannes Leuschner and Maximilian Schmidt and Daniel Otero Baguer and Peter Maaß},
  title = {LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction},
  journal = {Scientific Data},
  volume = {8},
  number = {109},
  year = {2021},
  doi = {10.1038/s41597-021-00893-z}
}


@ARTICLE{Leuschner2021_a4944,
  author = {Johannes Leuschner and Maximilian Schmidt and P.S. Ganguly and V Andriiashen and S.B. Coban and Alexander Denker and D. Bauer and A. Hadjifaradji and K.J. Batenburg and Bolko Maass and M. von Eijnatten},
  title = {Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications},
  journal = {MDPI Journal of Imaging},
  volume = {7},
  number = {3},
  pages = {44},
  year = {2021},
  doi = {10.3390/jimaging7030044}
}


@ARTICLE{OteroBaguer2020_8962c,
  author = {Daniel Otero Baguer and Johannes Leuschner and Maximilian Schmidt},
  title = {Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods},
  journal = {Inverse Problems},
  volume = {36},
  number = {9},
  publisher = {IOPscience},
  year = {2020},
  doi = {10.1088/1361-6420/aba415}
}


@ARTICLE{Leuschner2018_e5a4f,
  author = {Johannes Leuschner and Maximilian Schmidt and Pascal Fernsel and Delf Lachmund and Tobias Boskamp and Peter Maaß},
  title = {Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications},
  journal = {Bioinformatics},
  annote = {bty909},
  year = {2018},
  doi = {10.1093/bioinformatics/bty909}
}


@PHDTHESIS{Schmidt2022_89094,
  author = {Maximilian Schmidt},
  title = {Hybrid Deep Learning: How Combining Data-Driven and Model-Based Approaches Solves Inverse Problems in Computed Tomography and Beyond},
  school = {Universität Bremen},
  year = {2022},
  doi = {	10.26092/elib/1941}
}


@CONFERENCE{Herdt2024_43883,
  author = {Rudolf Herdt and Maximilian Schmidt and Daniel Otero Baguer and Jean Le Clerc Arrastia and Peter Maaß},
  title = {How GAN Generators can Invert Networks in Real-Time},
  booktitle = {The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023},
  volume = {222},
  pages = {422-437},
  year = {2024}
}


@CONFERENCE{Schmidt2021_8b8a3,
  author = {Maximilian Schmidt},
  title = {Around the clock - capsule networks and image transformations},
  booktitle = {PAMM},
  volume = {20},
  number = {1},
  pages = {e202000179},
  year = {2021},
  doi = {10.1002/pamm.202000179}
}


@CONFERENCE{Schmidt2021_ed1f3,
  author = {Maximilian Schmidt and Alexander Denker and Johannes Leuschner},
  title = {The Deep Capsule Prior - advantages through complexity},
  booktitle = {GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021},
  volume = {21},
  number = {1},
  year = {2021},
  doi = {10.1002/pamm.202100166}
}


@CONFERENCE{Denker2020_760c8,
  author = {Alexander Denker and Maximilian Schmidt and Johannes Leuschner and Peter Maaß and Jens Behrmann},
  title = {Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction},
  booktitle = {ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich},
  year = {2020}
}