World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
16190
World Ranking
2604
National Ranking
353

Feng Shi 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 Feng Shi 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: 207 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Feng Shi 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 Feng Shi 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

Feng Shi is affiliated with United Imaging Intelligence (China) and has a research focus predominantly in the field of medicine, with a strong emphasis on radiology, nuclear medicine, and imaging. Their scholarly output includes contributions to computer vision and pattern recognition, pulmonary and respiratory medicine, neurology, and artificial intelligence.

Their work extensively covers topics related to radiomics and machine learning in medical imaging, COVID-19 diagnosis using AI, functional brain connectivity studies, advanced neuroimaging techniques and applications, medical image segmentation techniques, advanced X-ray and CT imaging, and brain tumor detection and classification.

Frequent publication venues for their work include the following:

  • UNC Libraries
  • arXiv (Cornell University)
  • Research Square (Research Square)
  • IEEE Transactions on Medical Imaging
  • Frontiers in Oncology

Notable recent papers authored or co-authored by Feng Shi are:

  • "Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia" (2020), IEEE Transactions on Medical Imaging
  • "Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning" (2020), IEEE Transactions on Medical Imaging
  • "Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT" (2020), IEEE Journal of Biomedical and Health Informatics
  • "Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images" (2020), arXiv (Cornell University)
  • "What medical waste management system may cope With COVID-19 pandemic: Lessons from Wuhan" (2021), Resources Conservation and Recycling

Feng Shi has collaborated frequently with several researchers, including:

  • Dinggang Shen
  • Ying Wei
  • Jiaojiao Wu
  • Weili Lin
  • Yaozong Gao

Best Publications

  • Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

    Feng Shi;Jun Wang;Jun Shi;Ziyan Wu

  • 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

  • Infant brain atlases from neonates to 1- and 2-year-olds.

    Feng Shi;Pew Thian Yap;Guorong Wu;Hongjun Jia

  • Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies.

    Feng Shi;Bing Liu;Yuan Zhou;Chunshui Yu

  • Brain MRI super resolution using 3D deep densely connected neural networks

    Yuhua Chen;Yibin Xie;Zhengwei Zhou;Feng Shi

  • Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification

    Feng Shi;Liming Xia;Fei Shan;Dijia Wu

  • Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-level Densely Connected Network

    Yuhua Chen;Yuhua Chen;Feng Shi;Anthony G. Christodoulou;Yibin Xie

  • Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia

    Xi Ouyang;Jiayu Huo;Liming Xia;Fei Shan

  • LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations

    Feng Shi;Jian Cheng;Li Wang;Pew-Thian Yap

  • LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

    Li Wang;Yaozong Gao;Feng Shi;Gang Li

  • Thick visual cortex in the early blind

    Jiefeng Jiang;Wanlin Zhu;Wanlin Zhu;Feng Shi;Yong Liu

  • Brain anatomical networks in early human brain development.

    Yong Fan;Feng Shi;Jeffrey Keith Smith;Weili Lin

  • Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.

    Feng Shi;Liming Xia;Fei Shan;Bin Song

  • Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning

    Hengyuan Kang;Liming Xia;Fuhua Yan;Zhibin Wan

  • Neonatal Brain Image Segmentation in Longitudinal MRI Studies

    Feng Shi;Yong Fan;Songyuan Tang;John H. Gilmore

  • Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT

    Liang Sun;Zhanhao Mo;Fuhua Yan;Liming Xia

  • Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features

    Yang Li;Yaping Wang;Yaping Wang;Guorong Wu;Feng Shi

  • Mapping Region-Specific Longitudinal Cortical Surface Expansion from Birth to 2 Years of Age

    Gang Li;Jingxin Nie;Li Wang;Feng Shi

  • Segmentation of neonatal brain MR images using patch-driven level sets.

    Li Wang;Feng Shi;Gang Li;Yaozong Gao

  • Computational neuroanatomy of baby brains: A review.

    Gang Li;Li Wang;Pew Thian Yap;Fan Wang

  • Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images

    Zhenyu Tang;Wei Zhao;Xingzhi Xie;Zheng Zhong

  • Correction: Family Poverty Affects the Rate of Human Infant Brain Growth.

    Jamie L. Hanson;Nicole Hair;Dinggang G. Shen;Feng Shi

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Weili Lin
Weili Lin University of North Carolina at Chapel Hill
Gang Li
Gang Li University of North Carolina at Chapel Hill
Yaozong Gao
Yaozong Gao United Imaging Healthcare (China)
Pew Thian Yap
Pew Thian Yap University of North Carolina at Chapel Hill
Guorong Wu
Guorong Wu University of North Carolina at Chapel Hill
Xi Zhang
Xi Zhang Jilin University
Qian Wang
Qian Wang Shanghai Jiao Tong University
Tianzi Jiang
Tianzi Jiang Chinese Academy of Sciences
Weiping Qin
Weiping Qin Jilin University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you're interested in Computer Science but want additional skills or a broader education, several online degrees can enhance your career options. Many students consider pairing their technical background with business expertise through an mba online cheap, preparing them for management or startup roles in the tech sector.

For those who want to upskill quickly, there are 1 year master's programs available in various disciplines, including technology and business. These programs let you advance your credentials without spending years in school.

If speed and affordability are top priorities, you may want to explore the fastest degree to get online options. These degrees are designed for rapid completion and can lead to well-paying jobs in IT, business, and more.

Interested in the future of technology? Specialized degrees in ai empower students to work in fields like machine learning, robotics, and data science, opening doors to some of tech’s most sought-after careers.

Best Scientists Citing Feng Shi

Trending Scientists

Recently Published Articles