World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
27914
World Ranking
2525
National Ranking
342

Hao Chen 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 Hao Chen 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: 266 publications — 66th percentile

66% of scientists in this discipline score the same or lower.

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

Hao Chen 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 Hao Chen 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Hao Chen is affiliated with the Chinese University of Hong Kong in China and has a research profile spanning Medicine and Computer Science, with a strong emphasis on Medical Imaging and Artificial Intelligence applications.

The scientist has contributed extensively to both main and subfields of study, including:

  • Medicine
  • Computer Science

Within these broad disciplines, Hao Chen's subfields of research include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Surgery
  • Pulmonary and Respiratory Medicine

Research topics frequently addressed in Hao Chen's work are:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • COVID-19 diagnosis using AI
  • Retinal Imaging and Analysis
  • Advanced Image and Video Retrieval Techniques

Research outputs include papers published in notable venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • SSRN Electronic Journal
  • Medical Image Analysis
  • IEEE Journal of Biomedical and Health Informatics

The following are representative recent publications by Hao Chen that reflect their research focus:

  • The Liver Tumor Segmentation Benchmark (LiTS), 2022, Medical Image Analysis
  • FCOS: A Simple and Strong Anchor-free Object Detector, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation, 2020, IEEE Transactions on Medical Imaging
  • Towards a new generation of artificial intelligence in China, 2020, Nature Machine Intelligence

Hao Chen has frequently collaborated with several researchers in their field, including:

  • Pheng-Ann Heng
  • Kwang-Ting Cheng
  • Luyang Luo
  • Chunhua Shen
  • Huangjing Lin

Best Publications

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

    Xiaomeng Li;Hao Chen;Xiaojuan Qi;Qi Dou

  • Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.

    Arnaud Arindra Adiyoso Setio;Alberto Traverso;Thomas de Bel;Moira S.N. Berens

  • MagNet: A Two-Pronged Defense against Adversarial Examples

    Dongyu Meng;Hao Chen

  • Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

    Lequan Yu;Hao Chen;Qi Dou;Jing Qin

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • Gland segmentation in colon histology images: The GlaS challenge contest

    Korsuk Sirinukunwattana;Josien P.W. Pluim;Hao Chen;Xiaojuan Qi

  • VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

    Hao Chen;Qi Dou;Lequan Yu;Jing Qin

  • Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

    Qi Dou;Hao Chen;Lequan Yu;Lei Zhao

  • DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

    Hao Chen;Xiaojuan Qi;Lequan Yu;Pheng-Ann Heng

  • 3D deeply supervised network for automated segmentation of volumetric medical images.

    Qi Dou;Lequan Yu;Hao Chen;Yueming Jin

  • Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

    Qi Dou;Hao Chen;Lequan Yu;Jing Qin

  • From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

    Peter Bandi;Oscar Geessink;Quirine Manson;Marcory Van Dijk

  • DCAN: Deep contour-aware networks for object instance segmentation from histology images

    Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

  • A Multi-Organ Nucleus Segmentation Challenge

    Neeraj Kumar;Ruchika Verma;Deepak Anand;Yanning Zhou

  • Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

    Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sanchez;Bogdan J. Matuszewski

  • Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

    Hao Chen;Dong Ni;Jing Qin;Shengli Li

  • Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

    Cheng Chen;Qi Dou;Hao Chen;Jing Qin

  • Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images

    Lequan Yu;Xin Yang;Hao Chen;Jing Qin

  • Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

    Xi Wang;Hao Chen;Caixia Gan;Huangjing Lin

  • Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

Frequent Co-Authors

Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Qi Dou
Qi Dou Chinese University of Hong Kong
Jing Qin
Jing Qin Hong Kong Polytechnic University
Lequan Yu
Lequan Yu University of Hong Kong
Chi-Wing Fu
Chi-Wing Fu Chinese University of Hong Kong
Xiaojuan Qi
Xiaojuan Qi University of Hong Kong
Nasir M. Rajpoot
Nasir M. Rajpoot University of Warwick
Carol Y. Cheung
Carol Y. Cheung Chinese University of Hong Kong
Hsiao Chang Chan
Hsiao Chang Chan Chinese University of Hong Kong
Dong Ni
Dong Ni Shenzhen University

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