World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
13279
World Ranking
9957
National Ranking
396

Tal Arbel 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 Tal Arbel 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: 186 publications — 41st percentile

41% of scientists in this discipline score the same or lower.

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

Tal Arbel 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 Tal Arbel 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Tal Arbel is affiliated with McGill University in Canada and specializes in research at the intersection of computer science and medicine. Their work spans over 81 publications in computer science and 68 in medicine, with a focus on subfields such as artificial intelligence, radiology, nuclear medicine and imaging, computer vision and pattern recognition, health informatics, and neurology.

Their research topics primarily cover radiomics and machine learning in medical imaging, AI in cancer detection, artificial intelligence in healthcare and education, explainable artificial intelligence (XAI), brain tumor detection and classification, medical image segmentation techniques, and advanced neural network applications.

Tal Arbel has contributed scholarly work to various publication venues. Frequent venues include:

  • arXiv (Cornell University)
  • Nature Methods
  • The Journal of Machine Learning for Biomedical Imaging
  • Medical Image Analysis
  • IEEE Transactions on Medical Imaging

Co-authorship collaborations have featured repeatedly with the following researchers:

  • Brennan Nichyporuk
  • Douglas L. Arnold
  • Raghav Mehta
  • Jean-Pierre Falet
  • Sotirios A. Tsaftaris

Representative recent papers by Tal Arbel include:

  • "Metrics reloaded: recommendations for image analysis validation" (2024), published in Nature Methods
  • "Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation" (2023), published on arXiv (Cornell University)
  • "Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis" (2020), published in Scientific Reports
  • "Understanding metric-related pitfalls in image analysis validation" (2024), published in Nature Methods
  • "Common Limitations of Image Processing Metrics: A Picture Story" (2021), published on arXiv (Cornell University)

Tal Arbel has also contributed to academic books, including a publication with Springer Science+Business Media titled "Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis" (2020).

Best Publications

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

    M. Jorge Cardoso;Tal Arbel;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

    K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus

  • Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

    Tanya Nair;Doina Precup;Douglas L. Arnold;Tal Arbel

  • Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

    Aaron Carass;Snehashis Roy;Amod Jog;Jennifer L. Cuzzocreo

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

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

  • Evaluating intensity normalization on MRIs of human brain with multiple sclerosis.

    Mohak Shah;Yiming Xiao;Nagesh K. Subbanna;Simon J. Francis

  • Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis

    Aaron Carass;Snehashis Roy;Adrian Gherman;Jacob C. Reinhold

  • Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion

    M. Toews;T. Arbel

  • Feature-based morphometry: discovering group-related anatomical patterns.

    Matthew Toews;William M. Wells Iii;William M. Wells Iii;D. Louis Collins;Tal Arbel

  • Viewpoint selection by navigation through entropy maps

    T. Arbel;F.P. Ferrie

  • Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

    Rashed Karim;Pranav Bhagirath;Piet Claus;R. James Housden

  • BIAS: Transparent reporting of biomedical image analysis challenges.

    Lena Maier-Hein;Annika Reinke;Michal Kozubek;Anne L. Martel

  • IBIS: an OR ready open-source platform for image-guided neurosurgery

    Simon Drouin;Anna Kochanowska;Marta Kersten-Oertel;Ian J. Gerard

  • Entropy-based gaze planning

    Tal Arbel;Frank P Ferrie

  • Multi-Modal Image Registration Based on Gradient Orientations of Minimal Uncertainty

    Dante De Nigris;D. L. Collins;T. Arbel

  • Temporally Consistent Probabilistic Detection of New Multiple Sclerosis Lesions in Brain MRI

    Colm Elliott;Douglas L. Arnold;D. Louis Collins;Tal Arbel

  • Common Limitations of Image Processing Metrics: A Picture Story

    Annika Reinke;Matthias Eisenmann;Minu Dietlinde Tizabi;Carole H. Sudre

  • Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations.

    Tal Arbel;Xavier Morandi;Roch Comeau;D. Collins

  • Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes.

    Nagesh K. Subbanna;Doina Precup;D. Louis Collins;Tal Arbel

  • Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields

    Z. Karimaghaloo;M. Shah;S. J. Francis;D. L. Arnold

  • Automatic Non-linear MRI-Ultrasound Registration for the Correction of Intra-operative Brain Deformations

    Tal Arbel;Xavier Morandi;Roch M. Comeau;D. Louis Collins

  • Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

    M.J. Cardoso;T. Arbel;V. Cheplygina;S.-L. Lee

  • BIAS: Transparent reporting of biomedical image analysis challenges

    Lena Maier-Hein;Annika Reinke;Michal Kozubek;Anne L. Martel

Frequent Co-Authors

Douglas L. Arnold
Douglas L. Arnold Montreal Neurological Institute and Hospital
D. Louis Collins
D. Louis Collins McGill University
Doina Precup
Doina Precup McGill University
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Pierre Jannin
Pierre Jannin University of Rennes
Bennett A. Landman
Bennett A. Landman Vanderbilt University
Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center
Bram van Ginneken
Bram van Ginneken Radboud University
Klaus H. Maier-Hein
Klaus H. Maier-Hein German Cancer Research Center

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