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Holger R. Roth

D-Index & Metrics

Computer Science

D-Index
56
Citations
23923
World Ranking
3954
National Ranking
1880

Holger R. Roth 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 Holger R. Roth 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: 232 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Holger R. Roth 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 Holger R. Roth 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Holger R. Roth is affiliated with Nvidia in the United States. Their research spans the intersection of computer science and medicine, focusing particularly on the development and application of artificial intelligence in medical imaging and healthcare.

The main fields of study covered in their work include:

  • Computer Science
  • Medicine

Their subfields of study show deep engagement with:

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

Holger R. Roth's research covers a variety of specialized topics such as:

  • 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
  • Artificial Intelligence in Healthcare and Education
  • Medical Image Segmentation Techniques

Frequent coauthors include:

  • Daguang Xu
  • Dong Yang
  • Ziyue Xu
  • Wenqi Li
  • Andriy Myronenko

Holger R. Roth has contributed extensively to academic literature, with recent papers such as:

  • UNETR: Transformers for 3D Medical Image Segmentation, 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • 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
  • Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets, 2020, Nature Communications
  • Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation, 2020, IEEE Transactions on Medical Imaging

Key publication venues for their work include:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Abdominal Radiology

Holger R. Roth has also contributed to book publications under Springer Science+Business Media, including titles such as:

  • Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020
  • Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021

Best Publications

  • Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu

  • 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

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

    Unknown

  • DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

    Holger R. Roth;Le Lu;Amal Farag;Hoo-Chang Shin

  • Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

    Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao

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

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

  • A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    Holger R. Roth;Le Lu;Ari Seff;Kevin M. Cherry

  • 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

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

    Ling Zhang;Xiaosong Wang;Dong Yang;Thomas Sanford

  • Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience

    Masashi Misawa;Shin-ei Kudo;Yuichi Mori;Tomonari Cho

  • Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.

    Holger R. Roth;Le Lu;Nathan Lay;Adam P. Harrison

  • An application of cascaded 3D fully convolutional networks for medical image segmentation.

    Holger R. Roth;Hirohisa Oda;Xiangrong Zhou;Natsuki Shimizu

  • Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.

    Mingchen Gao;Ulas Bagci;Le Lu;Aaron Wu

  • 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

  • A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

    Holger R. Roth;Le Lu;Ari Seff;Kevin M. Cherry

  • 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

  • Anatomy-specific classification of medical images using deep convolutional nets

    Holger R. Roth;Christopher T. Lee;Hoo-Chang Shin;Ari Seff

  • Deep convolutional networks for pancreas segmentation in CT imaging

    Holger R. Roth;Amal Farag;Le Lu;Evrim B. Turkbey

  • A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

    Amal Farag;Le Lu;Holger R. Roth;Jiamin Liu

  • An application of cascaded 3D fully convolutional networks for medical image segmentation

    Holger R. Roth;Hirohisa Oda;Xiangrong Zhou;Natsuki Shimizu

  • Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

    Holger R. Roth;Le Lu;Amal Farag;Andrew Sohn

  • Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.

    Kao-Lang Liu;Tinghui Wu;Po-Ting Chen;Yuhsiang M Tsai

Frequent Co-Authors

Kensaku Mori
Kensaku Mori Nagoya University
Daguang Xu
Daguang Xu Nvidia (United Kingdom)
Ziyue Xu
Ziyue Xu Nvidia (United States)
Ronald M. Summers
Ronald M. Summers National Institutes of Health
Le Lu
Le Lu Alibaba Group (China)
David J. Hawkes
David J. Hawkes University College London
Steve Halligan
Steve Halligan University College London
Baris Turkbey
Baris Turkbey National Institutes of Health
Jianhua Yao
Jianhua Yao Tencent (China)
Greg Slabaugh
Greg Slabaugh Queen Mary University of London

External Links

Personal website of Holger R. Roth Google Scholar page

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