World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
79
Citations
25997
World Ranking
1140
National Ranking
21

Huazhu Fu 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 Huazhu Fu 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: 396 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Huazhu Fu 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 Huazhu Fu 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: 79 D-Index — 92nd percentile

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

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

Overview

Huazhu Fu is affiliated with the Agency for Science, Technology and Research in Singapore. Their research spans the intersection of computer science and medicine, focusing extensively on computer vision, pattern recognition, and medical imaging applications.

The scientist has contributed notably to the following main fields of study:

  • Computer Science
  • Medicine

Within these fields, Huazhu Fu has specialized in several subfields including:

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

Their work covers a range of topics closely related to medical imaging and analysis:

  • Retinal Imaging and Analysis
  • AI in cancer detection
  • Advanced Neural Network Applications
  • Glaucoma and retinal disorders
  • Medical Image Segmentation Techniques
  • Retinal Diseases and Treatments
  • Radiomics and Machine Learning in Medical Imaging

Huazhu Fu has co-authored frequently with several researchers, including:

  • Jiang Liu
  • Yanwu Xu
  • Ling Shao
  • Rick Siow Mong Goh
  • Yitian Zhao

The scientist has published extensively in venues focused on medical and biomedical imaging, with significant contributions appearing in:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • IEEE Journal of Biomedical and Health Informatics
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Huazhu Fu's recent papers include:

  • Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images, 2020, IEEE Transactions on Medical Imaging
  • Transformers in medical imaging: A survey, 2023, Medical Image Analysis
  • Salient Object Detection in the Deep Learning Era: An In-Depth Survey, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST, 2023, Nature Communications
  • Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis, 2020, IEEE Transactions on Medical Imaging

Additionally, Huazhu Fu has contributed to books published by Springer Science+Business Media, including several editions of Ophthalmic Medical Image Analysis and titles related to medical image computing and resource-efficient analysis. These books were published from 2020 through 2023.

Best Publications

  • CE-Net: Context Encoder Network for 2D Medical Image Segmentation

    Zaiwang Gu;Jun Cheng;Huazhu Fu;Kang Zhou

  • Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation

    Huazhu Fu;Jun Cheng;Yanwu Xu;Damon Wing Kee Wong

  • PraNet: Parallel Reverse Attention Network for Polyp Segmentation

    Deng-Ping Fan;Ge-Peng Ji;Tao Zhou;Geng Chen

  • Transformers in Medical Imaging: A Survey

    Unknown

  • Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

    Deng-Ping Fan;Tao Zhou;Ge-Peng Ji;Yi Zhou

  • Diversity-induced Multi-view Subspace Clustering

    Xiaochun Cao;Changqing Zhang;Huazhu Fu;Si Liu

  • Salient Object Detection in the Deep Learning Era: An In-depth Survey.

    Wenguan Wang;Qiuxia Lai;Huazhu Fu;Jianbing Shen

  • REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

    José Ignacio Orlando;Huazhu Fu;João Barbossa Breda;Karel van Keer

  • Generalized Latent Multi-View Subspace Clustering

    Changqing Zhang;Huazhu Fu;Qinghua Hu;Xiaochun Cao

  • Salient Object Detection in the Deep Learning Era: An In-Depth Survey

    Wenguan Wang;Qiuxia Lai;Huazhu Fu;Jianbing Shen

  • Low-Rank Tensor Constrained Multiview Subspace Clustering

    Changqing Zhang;Huazhu Fu;Si Liu;Guangcan Liu

  • Latent Multi-view Subspace Clustering

    Changqing Zhang;Qinghua Hu;Huazhu Fu;Pengfei Zhu

  • DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

    Huazhu Fu;Yanwu Xu;Stephen Lin;Damon Wing Kee Wong

  • Cluster-Based Co-Saliency Detection

    Huazhu Fu;Xiaochun Cao;Zhuowen Tu

  • Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image

    Huazhu Fu;Jun Cheng;Yanwu Xu;Changqing Zhang

  • Trusted Multi-View Classification With Dynamic Evidential Fusion

    Unknown

  • Review of Visual Saliency Detection With Comprehensive Information

    Runmin Cong;Jianjun Lei;Huazhu Fu;Ming-Ming Cheng

  • Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

    Unknown

  • Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis

    Tao Zhou;Huazhu Fu;Geng Chen;Jianbing Shen

  • Depth Enhanced Saliency Detection Method

    Yupeng Cheng;Huazhu Fu;Xingxing Wei;Jiangjian Xiao

  • FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction

    Unknown

  • CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging.

    Lei Mou;Yitian Zhao;Huazhu Fu;Yonghuai Liu

  • Applications of deep learning in fundus images: A review.

    Tao Li;Wang Bo;Chunyu Hu;Hong Kang

  • CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading

    Along He;Tao Li;Ning Li;Kai Wang

  • Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

    Huazhu Fu;Yanwu Xu;Damon Wing Kee Wong;Jiang Liu

  • Medical image analysis

    Baba C. Vemuri;James S. Duncan

Frequent Co-Authors

Jiang Liu
Jiang Liu Southern University of Science and Technology
Yanwu Xu
Yanwu Xu South China University of Technology
Xiaochun Cao
Xiaochun Cao Sun Yat-sen University
Jun Cheng
Jun Cheng University of Chinese Academy of Sciences
Changqing Zhang
Changqing Zhang Tianjin University
Jianbing Shen
Jianbing Shen University of Macau
Damon Wing Kee Wong
Damon Wing Kee Wong Nanyang Technological University
Runmin Cong
Runmin Cong Shandong University
Yitian Zhao
Yitian Zhao Chinese Academy of Sciences
Ling Shao
Ling Shao Terminus International

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