World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
12278
World Ranking
12359
National Ranking
786

Ozan Oktay 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 Ozan Oktay 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: 95 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Ozan Oktay 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 Ozan Oktay 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Ozan Oktay is affiliated with Imperial College London in the United Kingdom. Their research primarily intersects the fields of Computer Science and Medicine, with a significant focus on Artificial Intelligence and its applications within healthcare and medical imaging.

Their scholarly output includes contributions across several specialized subfields, notably Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Health Informatics, and Oncology. This diverse range reflects a multidisciplinary approach to medical and computational challenges.

The main topics of Ozan Oktay's work cover a variety of areas within medical imaging and artificial intelligence, such as Radiomics and Machine Learning in Medical Imaging, Topic Modeling, Artificial Intelligence in Healthcare and Education, Natural Language Processing Techniques, Multimodal Machine Learning Applications, COVID-19 diagnosis using AI, and Explainable Artificial Intelligence (XAI).

Some of their recent publications include:

  • "Active label cleaning for improved dataset quality under resource constraints," 2022, Nature Communications
  • "Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers," 2020, JAMA Network Open
  • "A causal perspective on dataset bias in machine learning for medical imaging," 2024, Nature Machine Intelligence
  • "Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank," 2020, Scientific Reports
  • "Exploring scalable medical image encoders beyond text supervision," 2025, Nature Machine Intelligence

Frequent co-authors collaborating with Ozan Oktay include:

  • Javier Alvarez-Valle
  • Daniel C. Castro
  • Anton Schwaighofer
  • Shruthi Bannur
  • Fernando Pérez-García

Ozan Oktay's publications are often found in both preprint and peer-reviewed venues. They have contributed extensively to arXiv (Cornell University) and have multiple publications in Nature Communications and Nature Machine Intelligence. Other publication venues include JAMA Network Open and Scientific Reports.

Best Publications

  • Attention U-Net: Learning Where to Look for the Pancreas

    Ozan Oktay;Jo Schlemper;Loïc Le Folgoc;Matthew C. H. Lee

  • Attention gated networks: Learning to leverage salient regions in medical images.

    Jo Schlemper;Ozan Oktay;Michiel Schaap;Mattias P. Heinrich

  • Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Wenjia Bai;Matthew Sinclair;Giacomo Tarroni;Ozan Oktay

  • DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

    Martin Rajchl;Matthew C. H. Lee;Ozan Oktay;Konstantinos Kamnitsas

  • Semi-supervised learning for network-based cardiac MR image segmentation

    Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki

  • Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

    Unknown

  • Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks

    Ozan Oktay;Wenjia Bai;Matthew C. H. Lee;Ricardo Guerrero

  • White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

    R. Guerrero;C. Qin;O. Oktay;C. Bowles

  • Evaluating reinforcement learning agents for anatomical landmark detection.

    Amir Alansary;Ozan Oktay;Yuanwei Li;Loic Le Folgoc

  • Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations

    Wenjia Bai;Hideaki Suzuki;Chen Qin;Giacomo Tarroni

  • Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

    Unknown

  • Active label cleaning for improved dataset quality under resource constraints

    Unknown

  • Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

    Olivier Bernard;Johan G. Bosch;Brecht Heyde;Martino Alessandrini

  • Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

    Maximilian Seitzer;Guang Yang;Jo Schlemper;Ozan Oktay

  • Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

    Jo Schlemper;Ozan Oktay;Liang Chen;Jacqueline Matthew

  • Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

    Jo Schlemper;Ozan Oktay;Liang Chen;Jacqueline Matthew

  • Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

    Ozan Oktay;Wenjia Bai;Ricardo Guerrero;Martin Rajchl

  • Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study

    Robert Robinson;Vanya V. Valindria;Wenjia Bai;Ozan Oktay

  • Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling

    Carlo Biffi;Ozan Oktay;Giacomo Tarroni;Wenjia Bai

  • Image-and-spatial transformer networks for structure-guided image registration

    Matthew C. H. Lee;Ozan Oktay;Andreas Schuh;Michiel Schaap

  • OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions.

    Mattias P. Heinrich;Ozan Oktay;Nassim Bouteldja

  • Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

    Robert Robinson;Vanya V. Valindria;Wenjia Bai;Ozan Oktay

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Wenjia Bai
Wenjia Bai Imperial College London
Ben Glocker
Ben Glocker Imperial College London
Bernhard Kainz
Bernhard Kainz Imperial College London
Mattias P. Heinrich
Mattias P. Heinrich University of Lübeck
Martin Rajchl
Martin Rajchl Imperial College London
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Paul M. Matthews
Paul M. Matthews Imperial College London
Steffen E. Petersen
Steffen E. Petersen Queen Mary University of London
Stuart A. Cook
Stuart A. Cook Duke NUS Graduate Medical School

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