World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
13855
World Ranking
3098
National Ranking
415

Jiang Liu 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 Jiang Liu 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: 569 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Jiang Liu 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 Jiang Liu 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

Jiang Liu is affiliated with the Southern University of Science and Technology in China. Their research primarily spans the fields of Medicine and Engineering, with a significant focus on subfields such as Radiology, Nuclear Medicine and Imaging, Ophthalmology, Computer Vision and Pattern Recognition, Biomedical Engineering, and Artificial Intelligence.

Their work covers various topics relating to medical imaging and analysis, particularly in retinal imaging and disorders. The main topics of their research include:

  • Retinal Imaging and Analysis
  • Glaucoma and retinal disorders
  • Retinal Diseases and Treatments
  • Optical Coherence Tomography Applications
  • Retinal and Optic Conditions
  • Advanced Neural Network Applications
  • Medical Image Segmentation Techniques

Jiang Liu has contributed to the scholarly community with numerous publications in leading venues. The most frequent publication venues are:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • SSRN Electronic Journal
  • Research Square (Research Square)
  • Medical Image Analysis

Their recent significant papers highlight contributions in cancer metastasis, cell death pathways, and medical imaging using deep learning. These include:

  • DNA of neutrophil extracellular traps promotes cancer metastasis via CCDC25, 2020, Nature
  • Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research, 2022, Journal of Hematology & Oncology
  • CS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging, 2020, Medical Image Analysis
  • ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model, 2020, IEEE Transactions on Medical Imaging
  • A map of transcriptional heterogeneity and regulatory variation in human microglia, 2021, Nature Genetics

Jiang Liu frequently collaborates with various researchers, including:

  • Huazhu Fu
  • Yitian Zhao
  • Yan Hu
  • Risa Higashita
  • Xiaoqing Zhang

Overall, their work integrates advanced neural networks and medical imaging technologies, emphasizing retinal health and associated diseases. Their multidisciplinary approach encompasses both clinical and computational aspects within biomedicine and engineering domains.

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

  • Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening

    Jun Cheng;Jiang Liu;Yanwu Xu;Fengshou Yin

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

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

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

    Huazhu Fu;Jun Cheng;Yanwu Xu;Changqing Zhang

  • Glaucoma detection based on deep convolutional neural network

    Xiangyu Chen;Yanwu Xu;Damon Wing Kee Wong;Tien Yin Wong

  • ORIGA -light : An online retinal fundus image database for glaucoma analysis and research

    Zhuo Zhang;Feng Shou Yin;Jiang Liu;Wing Kee Wong

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

    Lei Mou;Yitian Zhao;Huazhu Fu;Yonghuai Liu

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

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

  • ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

    Yuhui Ma;Huaying Hao;Jianyang Xie;Huazhu Fu

  • CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

    Lei Mou;Yitian Zhao;Li Chen;Jun Cheng

  • Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI

    D. W. K. Wong;J. Liu;J.H. Lim;X. Jia

  • Structure and Illumination Constrained GAN for Medical Image Enhancement.

    Yuhui Ma;Jiang Liu;Yonghuai Liu;Huazhu Fu

  • Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

    Huazhu Fu;Boyang Wang;Jianbing Shen;Shanshan Cui

  • Automatic Feature Learning for Glaucoma Detection Based on Deep Learning

    Xiangyu Chen;Yanwu Xu;Shuicheng Yan;Damon Wing Kee Wong

  • Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis

    Fengshou Yin;Jiang Liu;Damon Wing Kee Wong;Ngan Meng Tan

  • Dense Dilated Network With Probability Regularized Walk for Vessel Detection

    Lei Mou;Li Chen;Jun Cheng;Zaiwang Gu

  • A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

    Huazhu Fu;Huazhu Fu;Mani Baskaran;Mani Baskaran;Yanwu Xu;Yanwu Xu;Stephen Lin

  • Conceptualizing perceived affordances in social media interaction design

    Yuxiang Zhao;Jiang Liu;Jian Tang;Qinghua Zhu

  • Model-based optic nerve head segmentation on retinal fundus images

    Fengshou Yin;Jiang Liu;Sim Heng Ong;Ying Sun

  • PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

    Unknown

  • Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

    Yitian Zhao;Yalin Zheng;Yonghuai Liu;Yifan Zhao

  • Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

    Kang Zhou;Yuting Xiao;Jianlong Yang;Jun Cheng

  • Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

    Huazhu Fu;Boyang Wang;Jianbing Shen;Shanshan Cui

  • CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

    Lei Mou;Yitian Zhao;Huazhu Fu;Yonghuai Liu

  • ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

    Yuhui Ma;Huaying Hao;Huazhu Fu;Jiong Zhang

  • Medical image analysis

    Baba C. Vemuri;James S. Duncan

Frequent Co-Authors

Damon Wing Kee Wong
Damon Wing Kee Wong Nanyang Technological University
Tien Yin Wong
Tien Yin Wong Tsinghua University
Jun Cheng
Jun Cheng University of Chinese Academy of Sciences
Yanwu Xu
Yanwu Xu South China University of Technology
Yitian Zhao
Yitian Zhao Chinese Academy of Sciences
Tin Aung
Tin Aung National University of Singapore
Huazhu Fu
Huazhu Fu Agency for Science, Technology and Research
Joo-Hwee Lim
Joo-Hwee Lim Agency for Science, Technology and Research
Carol Y. Cheung
Carol Y. Cheung Chinese University of Hong Kong
Renchao Xie
Renchao Xie Beijing University of Posts and Telecommunications

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