World's Best Scientists 2026 revealed!
Francesco Ciompi

Francesco Ciompi

D-Index & Metrics

Computer Science

D-Index
35
Citations
21532
World Ranking
11414
National Ranking
212

Francesco Ciompi 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 Francesco Ciompi 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: 122 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.

Francesco Ciompi 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 Francesco Ciompi 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Francesco Ciompi is affiliated with Radboud University in the Netherlands. Their research primarily spans the fields of Medicine and Computer Science, with a strong focus on applications of Artificial Intelligence and machine learning techniques in medical contexts.

Their subfields of study include:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Oncology
  • Cancer Research
  • Molecular Biology

The main topics featured in Francesco Ciompi's work are:

  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Cancer Immunotherapy and Biomarkers
  • Colorectal Cancer Screening and Detection
  • Cancer Genomics and Diagnostics
  • Digital Imaging for Blood Diseases
  • Immunotherapy and Immune Responses

Francesco Ciompi has published in several academic venues, frequently contributing to:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • npj Breast Cancer
  • Modern Pathology
  • bioRxiv (Cold Spring Harbor Laboratory)

Frequent co-authors working alongside Francesco Ciompi include:

  • Jeroen van der Laak
  • Maschenka Balkenhol
  • John-Melle Bokhorst
  • Irıs D. Nagtegaal
  • Witali Aswolinskiy

Notable recent publications include:

  • "Deep learning in histopathology: the path to the clinic" (2021, Nature Medicine)
  • "HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images" (2021, Data Archiving and Networked Services (DANS))
  • "Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer" (2020, npj Breast Cancer)
  • "Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group" (2020, npj Breast Cancer)
  • "Mitosis domain generalization in histopathology images - The MIDOG challenge" (2022, Medical Image Analysis)

Best Publications

  • A survey on deep learning in medical image analysis

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

  • 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 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

  • 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

  • Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

    Francesco Ciompi;Bartjan de Hoop;Sarah J. van Riel;Kaman Chung

  • Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans

    Bram van Ginneken;Arnaud A. A. Setio;Colin Jacobs;Francesco Ciompi

  • Towards automatic pulmonary nodule management in lung cancer screening with deep learning

    Francesco Ciompi;Kaman Chung;Sarah J. van Riel;Arnaud Arindra Adiyoso Setio

  • Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

    Mitko Veta;Yujing J. Heng;Nikolas Stathonikos;Babak Ehteshami Bejnordi

  • Neural Image Compression for Gigapixel Histopathology Image Analysis

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

  • HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images.

    Mart van Rijthoven;Maschenka Balkenhol;Karina Siliņa;Jeroen van der Laak

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

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

  • Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

    Simone Balocco;Carlo Gatta;Francesco Ciompi;Andreas Wahle

  • Learning to detect lymphocytes in immunohistochemistry with deep learning

    Zaneta Swiderska-Chadaj;Hans Pinckaers;Mart van Rijthoven;Maschenka Balkenhol

  • Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

    Mohamed Amgad;Elisabeth Specht Stovgaard;Eva Balslev;Jeppe Thagaard

  • Improving airway segmentation in computed tomography using leak detection with convolutional networks.

    Jean-Paul Charbonnier;Eva M. van Rikxoort;Arnaud A.A. Setio;Cornelia M. Schaefer-Prokop

  • Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer

    Oscar G F Geessink;Alexi Baidoshvili;Joost M Klaase;Babak Ehteshami Bejnordi

  • Deep learning assisted mitotic counting for breast cancer.

    Maschenka C A Balkenhol;David Tellez;Willem Vreuls;Pieter C Clahsen

  • Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound

    J C Seabra;F Ciompi;O Pujol;J Mauri

  • HoliMAb: A holistic approach for Media–Adventitia border detection in intravascular ultrasound

    Francesco Ciompi;Oriol Pujol;Carlo Gatta;Marina Alberti

  • Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?

    Kaman Chung;Colin Jacobs;Ernst T. Scholten;Jin Mo Goo

Frequent Co-Authors

Petia Radeva
Petia Radeva University of Barcelona
Bram van Ginneken
Bram van Ginneken Radboud University
Geert Litjens
Geert Litjens Radboud University
Oriol Pujol
Oriol Pujol University of Barcelona
Mathias Prokop
Mathias Prokop Radboud University
Iris D. Nagtegaal
Iris D. Nagtegaal Radboud University
Ugo Pastorino
Ugo Pastorino University of Milan
Carsten Denkert
Carsten Denkert Philipp University of Marburg
Anant Madabhushi
Anant Madabhushi Emory University
Sherene Loi
Sherene Loi Peter MacCallum Cancer Centre

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