World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
43
Citations
6819
World Ranking
6221
National Ranking
204

Overview

Peter Maass is affiliated with the University of Bremen in Germany and has a significant research output across multiple interdisciplinary domains. Their work predominantly focuses on medicine, computer science, and engineering, with a considerable volume of publications in these fields.

The main subfields of their research include computer vision and pattern recognition, radiology, nuclear medicine and imaging, artificial intelligence, biomedical engineering, and geophysics.

Key topics covered in their studies span medical imaging techniques and applications, AI in cancer detection, image and signal denoising methods, advanced X-ray and CT imaging, nonmelanoma skin cancer studies, cutaneous melanoma detection and management, and generative adversarial networks and image synthesis.

Peter Maass has contributed to a range of peer-reviewed articles and research papers. Notable recent publications include:

  • Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma, 2021, Journal of Imaging
  • Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications, 2021, Journal of Imaging
  • LoDoPaB-CT Challenge Set, 2020, Zenodo (CERN European Organization for Nuclear Research)
  • Conditional Invertible Neural Networks for Medical Imaging, 2021, MDPI (MDPI AG)
  • Deep learning methods for partial differential equations and related parameter identification problems, 2023, Inverse Problems

Their publication record includes frequent appearances in venues such as arXiv (Cornell University), Inverse Problems, Journal of Imaging, European Journal of Cancer, and Cancers.

Peter Maass has collaborated extensively with several co-authors, including Alexander Denker, Daniel Otero Baguer, Maximilian Schmidt, Sören Dittmer, and Jean Le'Clerc Arrastia.

Best Publications

  • Solving inverse problems using data-driven models

    Simon R. Arridge;Peter Maass;Ozan Öktem;Carola-Bibiane Schönlieb

  • Wavelets: Theory and Applications

    Alfred Karl Louis;P Maass;A Rieder

  • A mollifier method for linear operator equations of the first kind

    A K Louis;P Maass

  • Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering

    Theodore Alexandrov;Michael Becker;Sören-Oliver Deininger;Günther Ernst

  • A reconstruction algorithm for electrical impedance tomography based on sparsity regularization

    Bangti Jin;Taufiquar Khan;Peter Maass

  • A Review of Some Modern Approaches to the Problem of Trend Extraction

    Theodore Alexandrov;Silvia Bianconcini;Estela Bee Dagum;Peter Maass

  • THE UNCERTAINTY PRINCIPLE ASSOCIATED WITH THE CONTINUOUS SHEARLET TRANSFORM

    Stephan Dahlke;Gitta Kutyniok;Peter Maass;Chen Sagiv

  • A generalized conditional gradient method and its connection to an iterative shrinkage method

    Kristian Bredies;Dirk A. Lorenz;Peter Maass

  • Regularization by Architecture: A Deep Prior Approach for Inverse Problems

    Sören Dittmer;Tobias Kluth;Peter Maass;Daniel Otero Baguer

  • Fast CG-Based Methods for Tikhonov--Phillips Regularization

    Andreas Frommer;Peter Maass

  • Sparsity regularization for parameter identification problems

    Bangti Jin;Peter Maass

  • Exploring three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry data: three-dimensional spatial segmentation of mouse kidney.

    Dennis Trede;Stefan Schiffler;Michael Becker;Stefan Wirtz

  • Deep Learning for Tumor Classification in Imaging Mass Spectrometry

    Jens Behrmann;Christian Etmann;Tobias Boskamp;Rita Casadonte

  • An analysis of electrical impedance tomography with applications to Tikhonov regularization

    Bangti Jin;Peter Maass

  • Sparsity reconstruction in electrical impedance tomography

    Matthias Gehre;Tobias Kluth;Antti Lipponen;Bangti Jin

  • Delay-range-dependent exponential H∞ synchronization of a class of delayed neural networks

    Hamid Reza Karimi;Peter Maass

  • Minimization of Tikhonov Functionals in Banach Spaces

    Thomas Bonesky;Kamil S. Kazimierski;Peter Maass;Frank Schöpfer

  • The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.

    Johannes Leuschner;Maximilian Schmidt;Daniel Otero Baguer;Peter Maaß

  • On the Connection Between Adversarial Robustness and Saliency Map Interpretability

    Christian Etmann;Sebastian Lunz;Peter Maass;Carola-Bibiane Schönlieb

  • The interior Radon transform

    Peter Maass

  • Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation

    Theodore Alexandrov;Jens Decker;Bart Mertens;Andre M. Deelder

Frequent Co-Authors

Theodore Alexandrov
Theodore Alexandrov European Molecular Biology Laboratory
Stephan Dahlke
Stephan Dahlke Philipp University of Marburg
Kristian Bredies
Kristian Bredies University of Graz
Carola-Bibiane Schönlieb
Carola-Bibiane Schönlieb University of Cambridge
Bangti Jin
Bangti Jin Chinese University of Hong Kong
Axel Walch
Axel Walch Technical University of Munich
Pierre Vandergheynst
Pierre Vandergheynst École Polytechnique Fédérale de Lausanne
Jason Cong
Jason Cong University of California, Los Angeles
Stefan Wirtz
Stefan Wirtz University of Erlangen-Nuremberg
Michel Salzet
Michel Salzet University of Lille

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