World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
20019
World Ranking
6293
National Ranking
46

Georg Langs 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 Georg Langs 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: 280 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Georg Langs 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 Georg Langs 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Georg Langs is affiliated with the Medical University of Vienna in Austria. Their primary field of study is Medicine, with a significant focus on Radiology, Nuclear Medicine and Imaging.

The scientist's research spans multiple subfields, including:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Pediatrics, Perinatology and Child Health
  • Cognitive Neuroscience
  • Pulmonary and Respiratory Medicine

Langs' main research topics encompass various advanced domains such as:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neuroimaging Techniques and Applications
  • Neonatal and fetal brain pathology
  • Functional Brain Connectivity Studies
  • Fetal and Pediatric Neurological Disorders
  • Domain Adaptation and Few-Shot Learning
  • AI in cancer detection

Their recent papers reflect a broad engagement with both methodological and applied research. Notable publications include:

  • "Introduction to Radiomics" (2020), published in Journal of Nuclear Medicine
  • "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans" (2020), published in Research Portal (King's College London)
  • "BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets" (2020), published in Communications Biology
  • "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem" (2020), published in European Radiology Experimental
  • "Cross-species functional alignment reveals evolutionary hierarchy within the connectome" (2020), published in NeuroImage

Langs collaborates frequently with a group of coauthors, including:

  • Karl-Heinz Nenning
  • Gregor Kasprian
  • Daniela Prayer
  • Helmut Prosch
  • Ernst Schwartz

The scientist's contributions are often published in select venues such as:

  • arXiv (Cornell University)
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • European Radiology
  • NeuroImage
  • Cerebral Cortex

Best Publications

  • Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

    Thomas Schlegl;Philipp Seeböck;Sebastian M. Waldstein;Ursula Schmidt-Erfurth

  • Situating the default-mode network along a principal gradient of macroscale cortical organization

    Daniel S. Margulies;Satrajit S. Ghosh;Satrajit S. Ghosh;Alexandros Goulas;Marcel Falkiewicz

  • Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

    Michael Roberts;Michael Roberts;Derek Driggs;Matthew Thorpe;Julian D. Gilbey

  • Causability and explainability of artificial intelligence in medicine.

    Andreas Holzinger;Georg Langs;Helmut Denk;Kurt Zatloukal

  • Introduction to Radiomics

    Marius E. Mayerhoefer;Marius E. Mayerhoefer;Andrzej Materka;Georg Langs;Ida Häggström

  • f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

    Thomas Schlegl;Philipp Seeböck;Sebastian M. Waldstein;Georg Langs

  • BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

    Reinder Vos de Wael;Oualid Benkarim;Casey Paquola;Sara Lariviere

  • Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning

    Thomas Schlegl;Sebastian M. Waldstein;Hrvoje Bogunovic;Franz Endstraßer

  • Parcellating cortical functional networks in individuals

    Danhong Wang;Randy L Buckner;Michael D Fox;Michael D Fox;Daphne J Holt

  • Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

    Johannes Hofmanninger;Florian Prayer;Jeanny Pan;Sebastian Rohrich

  • The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space

    Johanna Klughammer;Barbara Kiesel;Thomas Roetzer;Nikolaus Fortelny

  • Cross-species functional alignment reveals evolutionary hierarchy within the connectome.

    Ting Xu;Karl Heinz Nenning;Ernst Schwartz;Seok Jun Hong

  • Fast Active Appearance Model Search Using Canonical Correlation Analysis

    R. Donner;M. Reiter;G. Langs;P. Peloschek

  • Continuous Learning AI in Radiology: Implementation Principles and Early Applications.

    Oleg S. Pianykh;Georg Langs;Marc Dewey;Dieter R. Enzmann

  • Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks

    Oscar Jimenez-del-Toro;Henning Muller;Markus Krenn;Katharina Gruenberg

  • Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach.

    Hrvoje Bogunovic;Sebastian M Waldstein;Thomas Schlegl;Georg Langs

  • Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.

    Ursula Schmidt-Erfurth;Hrvoje Bogunovic;Amir Sadeghipour;Thomas Schlegl

  • Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

    Philipp Seebock;Jose Ignacio Orlando;Thomas Schlegl;Sebastian M. Waldstein

  • Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

    Georg Langs;Georg Langs;Polina Golland;Satrajit S. Ghosh;Satrajit S. Ghosh

  • Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data

    Philipp Seebock;Sebastian M. Waldstein;Sophie Klimscha;Hrvoje Bogunovic

  • Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks

    Thomas Schlegl;Sebastian M. Waldstein;Wolf-Dieter Vogl;Ursula Schmidt-Erfurth

Frequent Co-Authors

Ursula Schmidt-Erfurth
Ursula Schmidt-Erfurth Medical University of Vienna
Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Horst Bischof
Horst Bischof Graz University of Technology
Nikos Paragios
Nikos Paragios CentraleSupélec
Peter Brugger
Peter Brugger University of Zurich
Veronika Schöpf
Veronika Schöpf University of Graz
Zhuowen Tu
Zhuowen Tu University of California, San Diego

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