World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
9858
World Ranking
6159
National Ranking
813

Dong Ni 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 Dong Ni 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: 199 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Dong Ni 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 Dong Ni 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Dong Ni is affiliated with Shenzhen University in China and has contributed extensively to the fields of medicine and computer science. Their research primarily spans computer vision and pattern recognition, radiology, nuclear medicine and imaging, artificial intelligence, pediatrics, perinatology and child health, and biomedical engineering.

The scientist's research focuses on several main topics, including:

  • AI in cancer detection
  • Radiomics and machine learning in medical imaging
  • Medical image segmentation techniques
  • Domain adaptation and few-shot learning
  • Advanced neural network applications
  • Fetal and pediatric neurological disorders
  • Generative adversarial networks and image synthesis

Dong Ni has published recent papers in notable journals, illustrating ongoing contributions to medical image analysis and related domains. These papers include:

  • "A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging" (2020), Medical Image Analysis
  • "Segment anything model for medical images?" (2023), Medical Image Analysis
  • "Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis" (2022), Medical Image Analysis
  • "Boundary-rendering network for breast lesion segmentation in ultrasound images" (2022), Medical Image Analysis
  • "Bio-inspired lubricant drug delivery particles for the treatment of osteoarthritis" (2020), Nanoscale

Frequently, they have collaborated with a core group of coauthors which includes Xin Yang, Yuhao Huang, Ruobing Huang, Wufeng Xue, and Haoran Dou, reflecting sustained research partnerships.

Their work has been published most regularly in the following venues:

  • arXiv (Cornell University)
  • Medical Image Analysis
  • Medical Physics
  • Ultrasound in Medicine & Biology
  • IEEE Transactions on Medical Imaging

Best Publications

  • Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

    Jie Zhi Cheng;Dong Ni;Yi Hong Chou;Jing Qin

  • Deep Learning in Medical Ultrasound Analysis: A Review

    Shengfeng Liu;Yi Wang;Xin Yang;Baiying Lei

  • A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

    Zhaohan Xiong;Qing Xia;Zhiqiang Hu;Ning Huang

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

    Hao Chen;Dong Ni;Jing Qin;Shengli Li

  • Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning

    Youyi Song;Ling Zhang;Siping Chen;Dong Ni

  • Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features

    Zhen Yu;Xudong Jiang;Feng Zhou;Jing Qin

  • Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images

    Youyi Song;Ee-Leng Tan;Xudong Jiang;Jie-Zhi Cheng

  • FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks

    Lingyun Wu;Jie-Zhi Cheng;Shengli Li;Baiying Lei

  • Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks

    Hao Chen;Qi Dou;Dong Ni;Jie-Zhi Cheng

  • Ultrasound Standard Plane Detection Using a Composite Neural Network Framework

    Hao Chen;Lingyun Wu;Qi Dou;Jing Qin

  • Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

    Yi Wang;Dong Ni;Haoran Dou;Xiaowei Hu

  • Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound

    Yi Wang;Na Wang;Min Xu;Junxiong Yu

  • Automatic Localization and Identification of Vertebrae in Spine CT via a Joint Learning Model with Deep Neural Networks

    Hao Chen;Chiyao Shen;Jing Qin;Dong Ni

  • Dense Deconvolutional Network for Skin Lesion Segmentation

    Hang Li;Xinzi He;Feng Zhou;Zhen Yu

  • Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images

    Sihong Chen;Jing Qin;Xing Ji;Baiying Lei

  • Reversible watermarking scheme for medical image based on differential evolution

    Baiying Lei;Ee-Leng Tan;Siping Chen;Dong Ni

  • Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

    Yi Wang;Haoran Dou;Xiaowei Hu;Lei Zhu

  • A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei.

    Youyi Song;Ling Zhang;Siping Chen;Dong Ni

  • Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound

    Xin Yang;Lequan Yu;Shengli Li;Huaxuan Wen

  • Relational-Regularized Discriminative Sparse Learning for Alzheimer’s Disease Diagnosis

    Baiying Lei;Peng Yang;Tianfu Wang;Siping Chen

  • Deep Attentional Features for Prostate Segmentation in Ultrasound

    Yi Wang;Zijun Deng;Xiaowei Hu;Lei Zhu;Lei Zhu

Frequent Co-Authors

Tianfu Wang
Tianfu Wang Shenzhen University
Xin Yang
Xin Yang Sun Yat-sen University
Baiying Lei
Baiying Lei Shenzhen University
Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Jing Qin
Jing Qin Hong Kong Polytechnic University
Feng Zhou
Feng Zhou University of Michigan–Ann Arbor
Dinggang Shen
Dinggang Shen ShanghaiTech University
Lequan Yu
Lequan Yu University of Hong Kong
Hao Chen
Hao Chen Chinese University of Hong Kong
Yuanjin Zhao
Yuanjin Zhao Southeast University

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