World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
43
Citations
6819
World Ranking
6223
National Ranking
203

Peter Maass publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Peter Maass sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 192 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Peter Maass D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Peter Maass sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 43 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

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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