World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
62
Citations
14345
World Ranking
2924
National Ranking
172

Danail Stoyanov 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 Danail Stoyanov 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: 463 publications — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Danail Stoyanov 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 Danail Stoyanov 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: 62 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Overview

Danail Stoyanov is affiliated with University College London in the United Kingdom. Their research spans multiple disciplines, primarily focusing on medicine, engineering, and computer science, with a strong emphasis on surgery and biomedical engineering subfields.

Their work covers a wide range of topics including surgical simulation and training, colorectal cancer screening and detection, anatomy and medical technology, soft robotics and applications, robotics and sensor-based localization, artificial intelligence in healthcare and education, and advanced vision and imaging.

Recent publications by Danail Stoyanov include:

  • Robot-Assisted Minimally Invasive Surgery-Surgical Robotics in the Data Age (2022, Proceedings of the IEEE)
  • Surgical spectral imaging (2020, Medical Image Analysis)
  • Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy (2021, Medical Image Analysis)
  • Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit (2021, IEEE Robotics & Automation Magazine)
  • Gesture Recognition in Robotic Surgery: A Review (2021, IEEE Transactions on Biomedical Engineering)

Frequent co-authors in Danail Stoyanov's research include:

  • Sophia Bano
  • Hani J. Marcus
  • Francisco Vasconcelos
  • Laurence Lovat
  • Evangelos B. Mazomenos

The most common publication venues for their work are:

  • arXiv (Cornell University)
  • International Journal of Computer Assisted Radiology and Surgery
  • Medical Image Analysis
  • IEEE Robotics and Automation Letters
  • Endoscopy

Danail Stoyanov has also contributed to several book publications, primarily with Springer Science+Business Media, including seven books published in 2020 related to Medical Image Computing and Computer Assisted Intervention - MICCAI. One publication was also with Centre National de la Recherche Scientifique in the same domain and year.

Best Publications

  • Surgical data science for next-generation interventions.

    Lena Maier-Hein;Swaroop S. Vedula;Stefanie Speidel;Nassir Navab;Nassir Navab

  • Surgical device with an end effector assembly and system for monitoring of tissue before and after a surgical procedure

    Shobhit Arya;Neil T. Clancy;Daniel S. Elson;George B. Hanna

  • Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

    Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sanchez;Bogdan J. Matuszewski

  • Why rankings of biomedical image analysis competitions should be interpreted with care

    Lena Maier-Hein;Matthias Eisenmann;Annika Reinke;Sinan Onogur

  • Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery.

    Lena Maier-Hein;Peter Mountney;Adrien Bartoli;Haytham Elhawary

  • Surgical Data Science - from Concepts toward Clinical Translation

    Lena Maier-Hein;Lena Maier-Hein;Matthias Eisenmann;Duygu Sarikaya;Duygu Sarikaya;Keno März

  • Vision-based and marker-less surgical tool detection and tracking: a review of the literature

    David Bouget;Max Allan;Danail Stoyanov;Pierre Jannin

  • Real-time stereo reconstruction in robotically assisted minimally invasive surgery

    Danail Stoyanov;Marco Visentini Scarzanella;Philip Pratt;Guang-Zhong Yang

  • Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

    Yohannes Kassahun;Bingbin Yu;Abraham Temesgen Tibebu;Danail Stoyanov

  • Three-Dimensional Tissue Deformation Recovery and Tracking

    Peter Mountney;Danail Stoyanov;Guang-Zhong Yang

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

    Danail Stoyanov;Zeike Taylor;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • Robot-Assisted Minimally Invasive Surgery—Surgical Robotics in the Data Age

    Unknown

  • Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions.

    Omer F Ahmad;Omer F Ahmad;Antonio S Soares;Evangelos Mazomenos;Patrick Brandao

  • Surgical data science: Enabling next-generation surgery

    Lena Maier-Hein;S. Swaroop Vedula;Stefanie Speidel;Nassir Navab;Nassir Navab

  • Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures

    Danail Stoyanov;George P. Mylonas;Fani Deligianni;Ara Darzi

  • Frontiers of robotic endoscopic capsules: a review

    Gastone Ciuti;Renato Caliò;Domenico Camboni;Luca Neri

  • Fully convolutional neural networks for polyp segmentation in colonoscopy

    Patrick Brandao;Evangelos B. Mazomenos;Gastone Ciuti;Renato Caliò

  • Toward Detection and Localization of Instruments in Minimally Invasive Surgery

    M. Allan;S. Ourselin;S. Thompson;D. J. Hawkes

  • Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

    Sharib Ali;Mariia Dmitrieva;Noha M. Ghatwary;Sophia Bano

  • Utility of optical see-through head mounted displays in augmented reality-assisted surgery: A systematic review

    Unknown

  • Surgical spectral imaging

    Neil T. Clancy;Geoffrey Jones;Lena Maier-Hein;Daniel S. Elson

  • Simultaneous stereoscope localization and soft-tissue mapping for minimal invasive surgery

    Peter Mountney;Danail Stoyanov;Andrew Davison;Guang-Zhong Yang

  • Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction.

    L. Maier-Hein;A. Groch;A. Bartoli;S. Bodenstedt

  • ToolNet: Holistically-nested real-time segmentation of robotic surgical tools

    Luis C. Garcia-Peraza-Herrera;Wenqi Li;Lucas Fidon;Caspar Gruijthuijsen

  • 2017 Robotic Instrument Segmentation Challenge.

    Max Allan;Alexey Shvets;Thomas Kurmann;Zichen Zhang

Frequent Co-Authors

Sebastien Ourselin
Sebastien Ourselin King's College London
Tom Vercauteren
Tom Vercauteren King's College London
Jan Deprest
Jan Deprest KU Leuven
Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center
David J. Hawkes
David J. Hawkes University College London
Guang-Zhong Yang
Guang-Zhong Yang Shanghai Jiao Tong University
Matthew J. Clarkson
Matthew J. Clarkson University College London
Stefanie Speidel
Stefanie Speidel National Center for Tumor Diseases
Pierre Jannin
Pierre Jannin University of Rennes
Kurinchi Selvan Gurusamy
Kurinchi Selvan Gurusamy University College London

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