% This file was created by ZeTeM Website
 
% Johannes Leuschner




@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}
}


@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{Antorán2023_c9fff,
  author = {Javier Antorán and R. Barbano and Johannes Leuschner and José Miguel Hernández-Lobato and Bangti Jin},
  title = {Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior},
  journal = {Transactions on Machine Learning Research},
  volume = {12},
  year = {2023}
}


@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{Schulze2021_f88d6,
  author = {Sören Schulze and Johannes Leuschner and Emily King},
  title = {Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients},
  journal = {MDPI Open Access Journals Signals},
  volume = {2},
  number = {4},
  pages = {637-661},
  year = {2021},
  doi = {10.3390/signals2040039}
}


@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{Leuschner2023_2d5d8,
  author = {Johannes Leuschner},
  title = {Deep Learning for Computed Tomography Reconstruction: Learned Methods, Deep Image Prior, and Uncertaninty Estimation},
  school = {Universität Bremen},
  year = {2023},
  doi = {10.26092/elib/2704}
}


@CONFERENCE{Nittscher2023_5d5b1,
  author = {Marco Nittscher and M. F. Lameter and R. Barbano and Johannes Leuschner and Bangti Jin and Peter Maaß},
  title = {SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction},
  booktitle = {Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023},
  year = {2023}
}


@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}
}