World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
19656
World Ranking
3017
National Ranking
52

Wynne Hsu 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 Wynne Hsu 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: 309 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Wynne Hsu 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 Wynne Hsu 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: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Overview

Wynne Hsu is affiliated with the National University of Singapore in Singapore. Their research spans across the fields of Computer Science and Medicine, with a significant focus on Artificial Intelligence and its applications in healthcare.

Hsu has made contributions in several subfields, including:

  • Artificial Intelligence
  • Ophthalmology
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Endocrinology, Diabetes and Metabolism

Their main topics of research work cover a range of healthcare and biomedical informatics issues, specifically:

  • Machine Learning in Healthcare
  • Retinal Imaging and Analysis
  • Artificial Intelligence in Healthcare
  • Retinal Diseases and Treatments
  • Retinal and Optic Conditions
  • Biomedical Text Mining and Ontologies
  • Misinformation and Its Impacts

Hsu's frequent publication venues include:

  • arXiv (Cornell University)
  • The Lancet Digital Health
  • Scientific Reports
  • PLoS ONE
  • Journal of the American Medical Informatics Association

Their recent notable papers include:

  • A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre (2020), Nature Biomedical Engineering
  • Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study (2020), The Lancet Digital Health
  • A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations (2020), The Lancet Digital Health
  • Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies (2021), Journal of Biomedical Informatics
  • Personalizing Medication Recommendation with a Graph-Based Approach (2021), ACM Transactions on Information Systems

Wynne Hsu has frequently collaborated with other researchers, including:

  • Mong Li Lee
  • Ngiap Chuan Tan
  • Tien Yin Wong
  • Hao Sen Andrew Fang
  • Dejiang Xu

Best Publications

  • Integrating classification and association rule mining

    Bing Liu;Wynne Hsu;Yiming Ma

  • Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

    Daniel Shu Wei Ting;Daniel Shu Wei Ting;Carol Yim Lui Cheung;Carol Yim Lui Cheung;Gilbert Lim;Gavin Siew Wei Tan;Gavin Siew Wei Tan

  • Mining association rules with multiple minimum supports

    Bing Liu;Wynne Hsu;Yiming Ma

  • Pruning and summarizing the discovered associations

    Bing Liu;Wynne Hsu;Yiming Ma

  • Analyzing the subjective interestingness of association rules

    Bing Liu;Wynne Hsu;Wynne Hsu;Shu Chen;Yiming Ma

  • Current research in the conceptual design of mechanical products

    Wynne Hsu;Irene M.Y. Woon

  • Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

    Valentina Bellemo;Zhan W Lim;Gilbert Lim;Quang D Nguyen

  • A prime number labeling scheme for dynamic ordered XML trees

    X. Wu;M.L. Lee;W. Hsu

  • XClust: clustering XML schemas for effective integration

    Mong Li Lee;Liang Huai Yang;Wynne Hsu;Xia Yang

  • Using general impressions to analyze discovered classification rules

    Bing Liu;Wynne Hsu;Shu Chen

  • An integrated color-spatial approach to content-based image retrieval

    Wynne Hsu;S. T. Chua;H. H. Pung

  • Supporting frequent updates in R-trees: a bottom-up approach

    Mong Li Lee;Wynne Hsu;Christian S. Jensen;Bin Cui

  • An effective approach to detect lesions in color retinal images

    Huan Wang;Wynne Hsu;Kheng Guan Goh;Mong Li Lee

  • Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study

    Yuchen Xie;Quang D Nguyen;Haslina Hamzah;Gilbert Lim

  • A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

    Charumathi Sabanayagam;Dejiang Xu;Daniel Sw Ting;Simon Nusinovici

  • Post-analysis of learned rules

    Bing Liu;Wynne Hsu

  • Image Mining: Trends and Developments

    Wynne Hsu;Mong Li Lee;Ji Zhang

  • Finding interesting patterns using user expectations

    Bing Liu;Wynne Hsu;Lai-Fun Mun;Hing-Yan Lee

  • Mining relationships among interval-based events for classification

    Dhaval Patel;Wynne Hsu;Mong Li Lee

  • Image mining: issues, frameworks and techniques

    Ji Zhang;Wynne Hsu;Mong Li Lee

  • Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application

    Valentina Bellemo;Gilbert Lim;Tyler Hyungtaek Rim;Gavin S W Tan

Frequent Co-Authors

Mong Li Lee
Mong Li Lee National University of Singapore
Tien Yin Wong
Tien Yin Wong Tsinghua University
Bing Liu
Bing Liu University of Illinois at Chicago
Jie Jin Wang
Jie Jin Wang University of Sydney
Carol Y. Cheung
Carol Y. Cheung Chinese University of Hong Kong
Paul Mitchell
Paul Mitchell University of Sydney
Ching-Yu Cheng
Ching-Yu Cheng National University of Singapore
See-Kiong Ng
See-Kiong Ng National University of Singapore
Limsoon Wong
Limsoon Wong National University of Singapore
Elena Rochtchina
Elena Rochtchina University of Sydney

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