World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
32158
World Ranking
7342
National Ranking
118

Geert Litjens 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 Geert Litjens 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: 124 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Geert Litjens 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 Geert Litjens 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Geert Litjens is affiliated with Radboud University in the Netherlands and has a research focus that spans medicine and computer science, particularly in the areas of artificial intelligence, radiology, nuclear medicine, imaging, and oncology. Their work prominently intersects with the application of AI and machine learning in healthcare, especially for cancer detection and diagnosis.

Their publication record includes significant contributions to topics such as:

  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Artificial Intelligence in Healthcare and Education
  • Prostate Cancer Diagnosis and Treatment
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Pancreatic and Hepatic Oncology Research

Recent papers authored or co-authored by Geert Litjens include:

  • "The Medical Segmentation Decathlon" (2022) published in Nature Communications
  • "Deep learning in histopathology: the path to the clinic" (2021) published in Nature Medicine
  • "Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study" (2020) published in The Lancet Oncology
  • "The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma" (2020) published in The American Journal of Surgical Pathology
  • "Metrics reloaded: recommendations for image analysis validation" (2024) published in Nature Methods

Frequent co-authors collaborating with Geert Litjens include Jeroen van der Laak, Bram van Ginneken, Henkjan Huisman, Hans Pinckaers, and Michela Antonelli.

Publications by Geert Litjens often appear in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Medical Image Analysis
  • European Urology
  • IEEE Journal of Biomedical and Health Informatics

Geert Litjens has also authored a book titled "Diagnosing and staging of periampullary adenocarcinoma," published in 2024 by Radboud University Press eBooks.

Best Publications

  • A survey on deep learning in medical image analysis

    Geert J. S. Litjens;Thijs Kooi;Babak Ehteshami Bejnordi;Arnaud Arindra Adiyoso Setio

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • The 2005 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma

    Geert J.L.H. van Leenders;Theodorus H. van der Kwast;David J. Grignon;Andrew J. Evans

  • Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

    Arnaud Arindra Adiyoso Setio;Francesco Ciompi;Geert Litjens;Paul Gerke

  • Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

    Geert Litjens;Clara I. Sánchez;Nadya Timofeeva;Meyke Hermsen

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

  • Large scale deep learning for computer aided detection of mammographic lesions

    Thijs Kooi;Geert J. S. Litjens;Bram van Ginneken;Albert Gubern-Mérida

  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms

    Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello

  • Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.

    Geert J. S. Litjens;Robert Toth;Wendy J. M. van de Ven;Caroline Hoeks

  • Deep learning in histopathology: the path to the clinic

    Jeroen van der Laak;Jeroen van der Laak;Geert Litjens;Francesco Ciompi

  • Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

    David Tellez;Geert Litjens;Péter Bándi;Wouter Bulten

  • From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

    Peter Bandi;Oscar Geessink;Quirine Manson;Marcory Van Dijk

  • Computer-Aided Detection of Prostate Cancer in MRI

    Geert Litjens;Oscar Debats;Jelle Barentsz;Nico Karssemeijer

  • Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

    David Tellez;Maschenka Balkenhol;Irene Otte-Holler;Rob van de Loo

  • State-of-the-Art Deep Learning in Cardiovascular Image Analysis

    Geert Litjens;Francesco Ciompi;Jelmer M. Wolterink;Bob D. de Vos

  • Stain Specific Standardization of Whole-Slide Histopathological Images

    Babak Ehteshami Bejnordi;Geert Litjens;Nadya Timofeeva;Irene Otte-Holler

  • Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

    Mohsen Ghafoorian;Nico Karssemeijer;Tom Heskes;Inge W. M. van Uden

  • Neural Image Compression for Gigapixel Histopathology Image Analysis

    David Tellez;Geert Litjens;Jeroen van der Laak;Francesco Ciompi

  • Using deep learning to segment breast and fibroglandular tissue in MRI volumes

    Mehmet Ufuk Dalmış;Geert Litjens;Katharina Holland;Arnaud Setio

  • The importance of stain normalization in colorectal tissue classification with convolutional networks

    Francesco Ciompi;Oscar Geessink;Babak Ehteshami Bejnordi;Gabriel Silva de Souza

  • Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

    Babak Ehteshami Bejnordi;Guido C. A. Zuidhof;Maschenka Balkenhol;Meyke Hermsen

  • Automated Gleason Grading of Prostate Biopsies using Deep Learning.

    Wouter Bulten;Hans Pinckaers;Hester van Boven;Robert Vink

Frequent Co-Authors

Bram van Ginneken
Bram van Ginneken Radboud University
Nico Karssemeijer
Nico Karssemeijer Radboud University
Francesco Ciompi
Francesco Ciompi Radboud University
Jelle O. Barentsz
Jelle O. Barentsz Radboud University
Clara I. Sánchez
Clara I. Sánchez University of Amsterdam
Anant Madabhushi
Anant Madabhushi Emory University
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Bennett A. Landman
Bennett A. Landman Vanderbilt University
Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center

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