World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
11863
World Ranking
4809
National Ranking
144

Weidong Cai 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 Weidong Cai 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: 337 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Weidong Cai 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 Weidong Cai 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Weidong Cai is affiliated with the University of Sydney in Australia. Their research spans multiple fields primarily within computer science and medicine, with a notable focus on radiology, nuclear medicine, and imaging. Their work integrates advanced computational techniques with medical applications.

The main fields of study for Weidong Cai include:

  • Computer Science
  • Medicine

The subfields where they have made significant contributions are:

  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Biophysics
  • Pediatrics, Perinatology and Child Health

Their research focuses on several main topics which are:

  • Advanced Neuroimaging Techniques and Applications
  • Advanced MRI Techniques and Applications
  • Advanced Neural Network Applications
  • Cell Image Analysis Techniques
  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Fetal and Pediatric Neurological Disorders

Frequent co-authors who have collaborated extensively with Weidong Cai include:

  • Dongnan Liu
  • Chaoyi Zhang
  • Lauren J. O'Donnell
  • Yuqian Chen
  • Yogesh Rathi

The predominant venues for publishing Weidong Cai's work are:

  • arXiv (Cornell University)
  • Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Medical Image Analysis
  • bioRxiv (Cold Spring Harbor Laboratory)

Recent papers authored or co-authored by Weidong Cai include:

  • "Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "NFN: A novel network followed network for retinal vessel segmentation," 2020, Neural Networks
  • "PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images," 2020, IEEE Transactions on Medical Imaging
  • "Decompose to Adapt: Cross-Domain Object Detection Via Feature Disentanglement," 2022, IEEE Transactions on Multimedia
  • "BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets," 2023, Nature Methods

Best Publications

  • Medical image classification with convolutional neural network

    Qing Li;Weidong Cai;Xiaogang Wang;Yun Zhou

  • Early diagnosis of Alzheimer's disease with deep learning

    Siqi Liu;Sidong Liu;Weidong Cai;Sonia Pujol

  • Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

    Siqi Liu;Sidong Liu;Weidong Cai;Hangyu Che

  • Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

    Kamran Ghasedi Dizaji;Amirhossein Herandi;Cheng Deng;Weidong Cai

  • Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

    Yutong Xie;Yong Xia;Jianpeng Zhang;Yang Song

  • Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis

    Tiange Xiang;Chaoyi Zhang;Yang Song;Jianhui Yu

  • Robust saliency detection via regularized random walks ranking

    Changyang Li;Yuchen Yuan;Weidong Cai;Yong Xia

  • Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data.

    Ashnil Kumar;Jinman Kim;Weidong Cai;Michael J. Fulham;Michael J. Fulham

  • Feature-Based Image Patch Approximation for Lung Tissue Classification

    Yang Song;Weidong Cai;Yun Zhou;D. D. Feng

  • NFN+: A novel network followed network for retinal vessel segmentation.

    Yicheng Wu;Yong Xia;Yang Song;Yanning Zhang

  • Multiscale Network Followed Network Model for Retinal Vessel Segmentation

    Yicheng Wu;Yong Xia;Yang Song;Yanning Zhang

  • Reversion Correction and Regularized Random Walk Ranking for Saliency Detection.

    Yuchen Yuan;Changyang Li;Jinman Kim;Weidong Cai

  • Content-based retrieval of dynamic PET functional images

    Weidong Cai;Dagan Feng;R. Fulton

  • DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations

    Yuchen Yuan;Yuchen Yuan;Yi Shi;Changyang Li;Jinman Kim

  • Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders

    Sidong Liu;Weidong Cai;Siqi Liu;Fan Zhang

  • Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model

    Xiao Cai;Feiping Nie;Weidong Cai;Heng Huang

  • Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach

    Weihong Deng;Jiani Hu;Jun Guo;Weidong Cai

  • Network Pruning via Performance Maximization

    Shangqian Gao;Feihu Huang;Weidong Cai;Heng Huang

  • Vessel-Net: Retinal Vessel Segmentation Under Multi-path Supervision

    Yicheng Wu;Yong Xia;Yang Song;Donghao Zhang

  • 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

Yang Song
Yang Song California Institute of Technology
Dagan Feng
Dagan Feng University of Sydney
Heng Huang
Heng Huang University of Pittsburgh
Ron Kikinis
Ron Kikinis Brigham and Women's Hospital
Yong Xia
Yong Xia Northwestern Polytechnical University
Yue Wang
Yue Wang Zhejiang University
Hanchuan Peng
Hanchuan Peng Southeast University
Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Wojciech Chrzanowski
Wojciech Chrzanowski University of Sydney
Qing Li
Qing Li University of Sydney

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