World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
3681
World Ranking
9914
National Ranking
4163

Axel Wismüller publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Axel Wismüller sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 167 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Axel Wismüller D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Axel Wismüller sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Axel Wismüller is affiliated with the University of Rochester in the United States. Their research primarily focuses on interdisciplinary fields spanning Medicine, Neuroscience, and Computer Science. Within these main areas, they have contributed extensively to subfields such as Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology, and Health Informatics.

Their work addresses various specialized topics, including:

  • Functional Brain Connectivity Studies
  • Neural dynamics and brain function
  • Artificial Intelligence in Healthcare and Education
  • COVID-19 and healthcare impacts
  • Advanced MRI Techniques and Applications
  • Advanced Neuroimaging Techniques and Applications
  • Bayesian Modeling and Causal Inference

Wismüller has a number of publications in high-visibility venues, with a strong presence in the following:

  • arXiv (Cornell University)
  • Medical Imaging 2020: Computer-Aided Diagnosis
  • Scientific Reports
  • Journal of NeuroVirology
  • 2021 29th European Signal Processing Conference (EUSIPCO)

Some of their noteworthy papers include:

  • Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data, 2021, Scientific Reports
  • Detecting cognitive impairment in HIV-infected individuals using mutual connectivity analysis of resting state functional MRI, 2020, Journal of NeuroVirology
  • Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments, 2021, 2021 29th European Signal Processing Conference (EUSIPCO)
  • Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks, 2020, arXiv (Cornell University)
  • Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data, 2020, arXiv (Cornell University)

Collaborative efforts have been a significant aspect of their career. Frequent co-authors include:

  • M. Ali Vosoughi
  • Adora M. DSouza
  • Anas Z. Abidin
  • Larry Stockmaster
  • Ali Vosoughi

Best Publications

  • Cluster Analysis of Biomedical Image Time-Series

    Axel Wismüller;Oliver Lange;Dominik R. Dersch;Gerda L. Leinsinger

  • MRI tumor segmentation with densely connected 3D CNN

    Lele Chen;Yue Wu;Adora M. DSouza;Anas Z. Abidin

  • Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.

    Mahesh B. Nagarajan;Markus B. Huber;Thomas Schlossbauer;Gerda Leinsinger

  • Local exponential stability of competitive neural networks with different time scales

    Anke Meyer-Bäse;Sergei S. Pilyugin;Axel Wismüller;Simon Foo

  • Quantitative Comparison of Automatic and Interactive Methods for MRI–SPECT Image Registration of the Brain Based on 3-Dimensional Calculation of Error

    Thomas Pfluger;Christian Vollmar;Axel Wismüller;Stefan Dresel

  • Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

    Mahesh B. Nagarajan;Markus B. Huber;Thomas Schlossbauer;Gerda Leinsinger

  • Fully automated biomedical image segmentation by self-organized model adaptation

    Axel Wismüller;Frank Vietze;Johannes Behrends;Anke Meyer-Baese

  • The deformable feature map - a novel neurocomputing algorithm for adaptive plasticity in pattern analysis

    Axel Wismüller;Frank Vietze;Dominik R. Dersch;Johannes Behrends

  • Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

    Gerda Leinsinger;Thomas Schlossbauer;Michael Scherr;Oliver Lange

  • Segmentation with neural networks

    Axel Wismüller;Frank Vietze;Dominik R. Dersch

  • Prediction of Biomechanical Properties of Trabecular Bone in MR Images With Geometric Features and Support Vector Regression

    Markus B Huber;Sarah L Lancianese;M B Nagarajan;I Z Ikpot

  • Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series

    A. Wismuller;A. Meyer-Baese;O. Lange;M.F. Reiser

  • Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data

    Kerstin Bunte;Barbara Hammer;Axel Wismüller;Michael Biehl

  • Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization.

    Thomas Schlossbauer;Gerda Leinsinger;Axel Wismuller;Oliver Lange

  • Performance of topological texture features to classify fibrotic interstitial lung disease patterns.

    Markus B. Huber;Mahesh B. Nagarajan;Gerda Leinsinger;Roger Eibel

  • Neighbor embedding XOM for dimension reduction and visualization

    Kerstin Bunte;Barbara Hammer;Thomas Villmann;Michael Biehl

  • Computer-Aided Diagnosis in Phase Contrast Imaging X-Ray Computed Tomography for Quantitative Characterization of ex vivo Human Patellar Cartilage

    Mahesh B. Nagarajan;Paola Coan;Markus B. Huber;Paul C. Diemoz

  • Medical image compression using topology-preserving neural networks

    Anke Meyer-Bäse;Karsten Jancke;Axel Wismüller;Simon Foo

  • Tumor feature visualization with unsupervised learning.

    Tim Wilhelm Nattkemper;A Wismuller

  • Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

    Anas Z. Abidin;Botao Deng;Adora M. DSouza;Mahesh B. Nagarajan

  • Alteration of brain network topology in HIV-associated neurocognitive disorder: A novel functional connectivity perspective.

    Anas Z. Abidin;Adora M. DSouza;Mahesh B. Nagarajan;Lu Wang

  • Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization

    Kerstin Bunte;Barbara Hammer;Thomas Villmann;Michael Biehl

  • The Exploration Machine --- A Novel Method for Data Visualization

    Axel Wismüller

  • A Neural Network Approach to Functional MRI Pattern Analysis — Clustering of Time-Series by Hierarchical Vector Quantization

    Axel Wismüller;Dominik R. Dersch;Bernadette Lipinski;Klaus Hahn

Frequent Co-Authors

Dorothee P. Auer
Dorothee P. Auer University of Nottingham
Maximilian F. Reiser
Maximilian F. Reiser Ludwig-Maximilians-Universität München
Tim Wilhelm Nattkemper
Tim Wilhelm Nattkemper Bielefeld University
Helge Ritter
Helge Ritter Bielefeld University
Felix Eckstein
Felix Eckstein Paracelsus Medical University
Thomas Villmann
Thomas Villmann Hochschule Mittweida
Barbara Hammer
Barbara Hammer Bielefeld University
John J. Foxe
John J. Foxe University of Rochester
Thomas M. Link
Thomas M. Link University of California, San Francisco
Thomas D. Otto
Thomas D. Otto University of Glasgow

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