World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
16975
World Ranking
6304
National Ranking
376

Daguang Xu 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 Daguang Xu 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: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Daguang Xu 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 Daguang Xu 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

Daguang Xu is affiliated with Nvidia in the United Kingdom. Their research spans the intersection of computer science and medicine, with a strong focus on artificial intelligence and medical imaging.

The primary fields of study for Daguang Xu include:

  • Computer Science
  • Medicine

Their subfields of study consist of:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Pulmonary and Respiratory Medicine
  • Biomedical Engineering

Key research topics covered in their work include:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Privacy-Preserving Technologies in Data
  • AI in cancer detection
  • Domain Adaptation and Few-Shot Learning
  • Medical Image Segmentation Techniques

Daguang Xu has contributed to several recent papers with publication dates mostly in 2022. Highlights include:

  • UNETR: Transformers for 3D Medical Image Segmentation, 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • The Medical Segmentation Decathlon, 2022, Nature Communications
  • The Liver Tumor Segmentation Benchmark (LiTS), 2022, Medical Image Analysis
  • Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Federated learning for predicting clinical outcomes in patients with COVID-19, 2021, Nature Medicine

Frequent collaborators of Daguang Xu include:

  • Holger R. Roth
  • Dong Yang
  • Ziyue Xu
  • Andriy Myronenko
  • Wenqi Li

The consistent venues for their publications are:

  • arXiv (Cornell University)
  • Medical Image Analysis
  • IEEE Transactions on Medical Imaging
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • The Journal of Urology

Daguang Xu has also contributed book chapters published by Springer Science+Business Media. Titles associated with these contributions include:

  • Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (2020)
  • Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (2021)
  • Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning (2021)

Best Publications

  • The future of digital health with federated learning

    Nicola Rieke;Nicola Rieke;Jonny Hancox;Wenqi Li;Fausto Milletari

  • UNETR: Transformers for 3D Medical Image Segmentation

    Unknown

  • Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

    Unknown

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

  • Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

    Unknown

  • Federated learning for predicting clinical outcomes in patients with COVID-19.

    Ittai Dayan;Holger R. Roth;Aoxiao Zhong;Ahmed Harouni

  • Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

    Stephanie A. Harmon;Thomas H. Sanford;Sheng Xu;Evrim B. Turkbey

  • Privacy-Preserving Federated Brain Tumour Segmentation

    Wenqi Li;Fausto Milletarì;Daguang Xu;Nicola Rieke

  • Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation

    Ling Zhang;Xiaosong Wang;Dong Yang;Thomas Sanford

  • Combo loss: Handling input and output imbalance in multi-organ segmentation

    Saeid Asgari Taghanaki;Saeid Asgari Taghanaki;Yefeng Zheng;S. Kevin Zhou;Bogdan Georgescu

  • VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

    Anjany Sekuboyina;Malek E. Husseini;Amirhossein Bayat;Maximilian Löffler

  • When Radiology Report Generation Meets Knowledge Graph

    Yixiao Zhang;Xiaosong Wang;Ziyue Xu;Qihang Yu

  • Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

    Dong Yang;Ziyue Xu;Wenqi Li;Andriy Myronenko

  • Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

    Yingda Xia;Dong Yang;Zhiding Yu;Fengze Liu

  • UNETR: Transformers for 3D Medical Image Segmentation

    Ali Hatamizadeh;Dong Yang;Holger Roth;Daguang Xu

  • Federated learning improves site performance in multicenter deep learning without data sharing.

    Karthik V Sarma;Stephanie Harmon;Thomas Sanford;Holger R Roth

  • Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

    Dong Yang;Daguang Xu;S. Kevin Zhou;Bogdan Georgescu

  • 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

    Yingda Xia;Fengze Liu;Dong Yang;Jinzheng Cai

  • Automatic Liver Segmentation Using Adversarial Image-to-Image Network

    Dong Yang;Daguang Xu;Shaohua Kevin Zhou;Bogdan Georgescu

  • C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation

    Qihang Yu;Dong Yang;Holger Roth;Yutong Bai

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Unknown

  • Federated Learning for Breast Density Classification: A Real-World Implementation.

    Holger R. Roth;Ken Chang;Praveer Singh;Nir Neumark

  • Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

    Dong Yang;Tao Xiong;Daguang Xu;Qiangui Huang

  • Method and System for Image Registration Using an Intelligent Artificial Agent

    Rui Liao;Shun Miao;Pierre de Tournemire;Julian Krebs

  • 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

    Siqi Liu;Daguang Xu;S. Kevin Zhou;Thomas Mertelmeier

Frequent Co-Authors

Holger R. Roth
Holger R. Roth Nvidia (United States)
Ziyue Xu
Ziyue Xu Nvidia (United States)
Baris Turkbey
Baris Turkbey National Institutes of Health
Dorin Comaniciu
Dorin Comaniciu Siemens (United States)
Bradford J. Wood
Bradford J. Wood National Institutes of Health
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Jin U. Kang
Jin U. Kang Johns Hopkins University
S. Kevin Zhou
S. Kevin Zhou University of Science and Technology of China
Peter L. Choyke
Peter L. Choyke National Institutes of Health
Bogdan Georgescu
Bogdan Georgescu Princeton University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education can be a practical route for those interested in computer science or related fields. Many students start with an online associate's degree, offering a flexible entry point and foundational knowledge before advancing to higher degrees or careers.

Affordability and speed are often top priorities. If you’re concerned about expenses or want to accelerate your learning, there are many cheap online degrees fast—programs that deliver quality on a budget and let you finish at your own pace.

Worried about your academic record? It’s reassuring to know that there are will grad schools accept low gpa resources that help identify online colleges flexible with admissions requirements and focused on student potential.

Finally, a computer science background opens up diverse professional options—much like what you’ll find for other STEM disciplines. You might be curious about the variety of jobs for environmental science majors; similarly, computer science graduates can pursue roles in cybersecurity, software development, IT support, and beyond.

Best Scientists Citing Daguang Xu

Trending Scientists