World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
23112
World Ranking
2414
National Ranking
1208

Bennett A. Landman 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 Bennett A. Landman 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: 470 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Bennett A. Landman 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 Bennett A. Landman 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: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Overview

Bennett A. Landman is affiliated with Vanderbilt University in the United States. Their research primarily spans the domain of Medicine, with a significant focus on Radiology, Nuclear Medicine and Imaging. Specialized subfields of study also include Cognitive Neuroscience, Artificial Intelligence, Computer Vision and Pattern Recognition, and Pulmonary and Respiratory Medicine.

The scientist's work covers several main topics including Advanced Neuroimaging Techniques and Applications, Advanced MRI Techniques and Applications, Functional Brain Connectivity Studies, Radiomics and Machine Learning in Medical Imaging, MRI in cancer diagnosis, AI in cancer detection, and Advanced Neural Network Applications.

Frequent collaborators in Bennett A. Landman's research include Kurt G. Schilling, Yuankai Huo, Shunxing Bao, Timothy J. Hohman, and Derek B. Archer.

Publication venues where Bennett A. Landman has contributed extensively feature:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Alzheimer's & Dementia
  • Journal of Medical Imaging
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

Bennett A. Landman has co-authored numerous papers, including recent publications such as:

  • UNETR: Transformers for 3D Medical Image Segmentation (2022), presented at the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • The Medical Segmentation Decathlon (2022), published in Nature Communications
  • Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Metrics reloaded: recommendations for image analysis validation (2024), published in Nature Methods
  • Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives (2023), published in Medical Image Analysis

In addition to journal articles and conference papers, Bennett A. Landman has contributed to book publications under Springer Science+Business Media. Titles include:

  • 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

  • The future of digital health with federated learning

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

  • 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

  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms

    Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello

  • Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments.

    Mina Kim;Mina Kim;Joseph Gillen;Joseph Gillen;Bennett A. Landman;Jinyuan Zhou;Jinyuan Zhou

  • Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

    Neda Jahanshad;Peter V. Kochunov;Emma Sprooten;Emma Sprooten;René C. Mandl

  • Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.

    Bennett A. Landman;Jonathan A.D. Farrell;Jonathan A.D. Farrell;Craig K. Jones;Craig K. Jones;Seth A. Smith;Seth A. Smith

  • Federated learning enables big data for rare cancer boundary detection

    Unknown

  • SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

    Yuankai Huo;Zhoubing Xu;Hyeonsoo Moon;Shunxing Bao

  • Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T

    Jonathan A.D. Farrell;Bennett A. Landman;Craig K. Jones;Craig K. Jones;Seth A. Smith;Seth A. Smith

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

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

  • Multi-parametric neuroimaging reproducibility: a 3-T resource study.

    Bennett A. Landman;Bennett A. Landman;Alan J. Huang;Alan J. Huang;Aliya Gifford;Deepti S. Vikram;Deepti S. Vikram

  • 3D whole brain segmentation using spatially localized atlas network tiles

    Yuankai Huo;Zhoubing Xu;Yunxi Xiong;Katherine Aboud

  • Deep learning for brain tumor classification

    Justin S. Paul;Andrew J. Plassard;Bennett A. Landman;Daniel Fabbri

  • Non-local statistical label fusion for multi-atlas segmentation

    Andrew J. Asman;Bennett A. Landman

  • Limits to anatomical accuracy of diffusion tractography using modern approaches.

    Kurt G. Schilling;Vishwesh Nath;Colin Hansen;Prasanna Parvathaneni

  • Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data

    Peter Kochunov;Neda Jahanshad;Daniel Marcus;Anderson Winkler

  • Histological validation of diffusion MRI fiber orientation distributions and dispersion.

    Kurt G. Schilling;Vaibhav A. Janve;Yurui Gao;Iwona Stepniewska

  • Synthesized b0 for diffusion distortion correction (Synb0-DisCo).

    Kurt G. Schilling;Justin Blaber;Yuankai Huo;Allen Newton

  • Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking.

    Mengyang Zhao;Aadarsh Jha;Quan Liu;Bryan A. Millis

  • Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT

    Zhoubing Xu;Christopher P. Lee;Mattias P. Heinrich;Marc Modat

  • The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software

    Blake C. Lucas;Blake C. Lucas;John A. Bogovic;Aaron Carass;Pierre Louis Bazin

  • Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI

    Bennett A. Landman;Bennett A. Landman;John A. Bogovic;Hanlin Wan;Fatma El Zahraa ElShahaby

  • Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

    N. Jahanshad;P. V. Kochunov;E. Sprooten;R. C. Mandl

Frequent Co-Authors

Yuankai Huo
Yuankai Huo Vanderbilt University
Susan M. Resnick
Susan M. Resnick National Institutes of Health
Jerry L. Prince
Jerry L. Prince Johns Hopkins University
Baxter P. Rogers
Baxter P. Rogers Vanderbilt University
Neil D. Woodward
Neil D. Woodward Vanderbilt University Medical Center
Iwona Stepniewska
Iwona Stepniewska Vanderbilt University
Warren D. Taylor
Warren D. Taylor Vanderbilt University Medical Center
Lori L. Beason-Held
Lori L. Beason-Held National Institutes of Health
David H. Zald
David H. Zald Rutgers, The State University of New Jersey
Laurie E. Cutting
Laurie E. Cutting Vanderbilt University

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