World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
18716
World Ranking
2313
National Ranking
1156

Dmitry B. Goldgof 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 Dmitry B. Goldgof 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: 380 publications — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Dmitry B. Goldgof 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 Dmitry B. Goldgof 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: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2017 - Fellow of the Indian National Academy of Engineering (INAE)
  • 2016 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2010 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to Computer Vision Pattern Recognition and Biomedical Engineering
  • 2007 - IEEE Fellow For contributions to computer vision and biomedical applications

Overview

Dmitry B. Goldgof is affiliated with the University of South Florida in the United States and has conducted extensive research in the domains of Medicine and Computer Science. Their work spans several subfields including Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pediatrics, Perinatology and Child Health, Pulmonary and Respiratory Medicine, and Computer Vision and Pattern Recognition.

The scientist's research covers significant topics such as Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, COVID-19 diagnosis using AI, Cell Image Analysis Techniques, Pediatric Pain Management Techniques, Infant Health and Development, and Lung Cancer Diagnosis and Treatment.

Frequent co-authors in their collaborative projects include Lawrence Hall, Peter R. Mouton, Saeed Alahmari, Ghada Zamzmi, and Md Sirajus Salekin.

Key publication venues where Dmitry B. Goldgof has contributed multiple works include:

  • arXiv (Cornell University)
  • IEEE Access
  • Tomography
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Computers in Biology and Medicine

Notable recent papers authored or co-authored by Dmitry B. Goldgof include:

  • Challenges for the Repeatability of Deep Learning Models, 2020, IEEE Access
  • Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets, 2020, Tomography
  • Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment, 2020, Computers in Biology and Medicine
  • Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions, 2023, ACM Computing Surveys
  • Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification, 2021, IEEE Access

Their contributions have been recognized through several fellowships and awards including:

  • Fellow of the Indian National Academy of Engineering (INAE), 2017
  • Fellow of the American Association for the Advancement of Science (AAAS), 2016
  • Fellow of the International Association for Pattern Recognition (IAPR), 2010, for contributions to Computer Vision Pattern Recognition and Biomedical Engineering
  • IEEE Fellow, 2007, for contributions to computer vision and biomedical applications

Best Publications

  • Radiomics: the process and the challenges

    Virendra Kumar;Yuhua Gu;Satrajit Basu;Anders Berglund

  • An experimental comparison of range image segmentation algorithms

    A. Hoover;G. Jean-Baptiste;X. Jiang;P.J. Flynn

  • Automatic tumor segmentation using knowledge-based techniques

    M.C. Clark;L.O. Hall;D.B. Goldgof;R. Velthuizen

  • Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol

    R. Kasturi;D. Goldgof;P. Soundararajan;V. Manohar

  • Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

    M. Zhou;J. Scott;B. Chaudhury;L. Hall

  • Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms

    J. Candamo;M. Shreve;D.B. Goldgof;D.B. Sapper

  • Automatic segmentation of non-enhancing brain tumors in magnetic resonance images

    Lynn M Fletcher-Heath;Lawrence O Hall;Dmitry B Goldgof;F.Reed Murtagh

  • Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

    Yoganand Balagurunathan;Yuhua Gu;Hua Wang;Virendra Kumar

  • Active Learning to Recognize Multiple Types of Plankton

    Tong Luo;Kurt Kramer;Dmitry B. Goldgof;Lawrence O. Hall

  • Deformable models in medical image analysis

    T. McInerney;D. Terzopoulos

  • MRI segmentation using fuzzy clustering techniques

    M.C. Clark;L.O. Hall;D.B. Goldgof;L.P. Clarke

  • Fast accurate fuzzy clustering through data reduction

    S. Eschrich;Jingwei Ke;L.O. Hall;D.B. Goldgof

  • Test–Retest Reproducibility Analysis of Lung CT Image Features

    Yoganand Balagurunathan;Virendra Kumar;Yuhua Gu;Jongphil Kim

  • Macro- and micro-expression spotting in long videos using spatio-temporal strain

    Matthew Shreve;Sridhar Godavarthy;Dmitry Goldgof;Sudeep Sarkar

  • Knowledge-based classification and tissue labeling of MR images of human brain

    Chunlin Li;D.B. Goldgof;L.O. Hall

  • Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging.

    A. A. Maudsley;A. Darkazanli;J. R. Alger;L. O. Hall

  • Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

    Lawrence O. Hall;Rahul Paul;Dmitry B. Goldgof;Gregory M. Goldgof

  • Fast fuzzy clustering

    Tai Wai Cheng;Dmitry B. Goldgof;Lawrence O. Hall

  • Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.

    Rahul Paul;Samuel H Hawkins;Yoganand Balagurunathan;Matthew B Schabath

  • Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images

    S. Kumar;D. Goldgof

Frequent Co-Authors

Lawrence O. Hall
Lawrence O. Hall University of South Florida
Robert J. Gillies
Robert J. Gillies Moffitt Cancer Center
Sudeep Sarkar
Sudeep Sarkar University of South Florida
Robert A. Gatenby
Robert A. Gatenby Moffitt Cancer Center
Rangachar Kasturi
Rangachar Kasturi University of South Florida
Kevin W. Bowyer
Kevin W. Bowyer University of Notre Dame
Chandra Kambhamettu
Chandra Kambhamettu University of Delaware
Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Jeffrey P. Krischer
Jeffrey P. Krischer University of South Florida
Hugo J.W.L. Aerts
Hugo J.W.L. Aerts Brigham and Women's Hospital

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