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Computer Science

D-Index
47
Citations
10546
World Ranking
6417
National Ranking
854

Yaozong Gao 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 Yaozong Gao 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: 148 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

Yaozong Gao 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 Yaozong Gao 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Yaozong Gao is affiliated with United Imaging Healthcare (China) and has a substantial record of research in medical imaging and AI applications within healthcare. Their work spans fields primarily focused on Medicine and Computer Science, with significant contributions to subfields such as Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Biomedical Engineering, Pulmonary and Respiratory Medicine, and Artificial Intelligence.

Their research particularly concentrates on advanced methods in medical imaging analysis, including radiomics, machine learning, and COVID-19 diagnosis supported by artificial intelligence. Key topics include:

  • Radiomics and Machine Learning in Medical Imaging
  • COVID-19 diagnosis using AI
  • Medical Imaging and Analysis
  • Medical Image Segmentation Techniques
  • Medical Imaging Techniques and Applications
  • Lung Cancer Diagnosis and Treatment
  • Brain Tumor Detection and Classification

Yaozong Gao has authored numerous papers published in diverse venues, frequently collaborating with notable researchers. Their major publication venues include:

  • UNC Libraries
  • IEEE Transactions on Medical Imaging
  • arXiv (Cornell University)
  • Medical Image Analysis
  • Neuro-Oncology

Their recent notable papers include:

  • "Lung Infection Quantification of COVID-19 in CT Images with Deep Learning" (2020, arXiv)
  • "Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia" (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)
  • "Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction" (2020, Medical Physics)
  • "Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images" (2021, Pattern Recognition)

Frequent collaborators in Gao's research include:

  • Dinggang Shen
  • Feng Shi
  • Fei Shan
  • Ying Wei
  • Xiaohuan Cao

The combination of Gao's work reveals a consistent focus on leveraging computational techniques such as deep learning and feature selection to improve diagnostic accuracy and quantitative analysis in medical imaging, particularly related to pulmonary diseases like COVID-19 and lung cancer.

Best Publications

  • 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

  • Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

    Fei Shan;Yaozong Gao;Jun Wang;Weiya Shi

  • The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.

    Nicholas Heller;Fabian Isensee;Klaus H. Maier-Hein;Xiaoshuai Hou

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

    Feng Shi;Liming Xia;Fei Shan;Dijia Wu

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

    Xi Ouyang;Jiayu Huo;Liming Xia;Fei Shan

  • Fully convolutional networks for multi-modality isointense infant brain image segmentation

    Dong Nie;Li Wang;Yaozong Gao;Dinggang Sken

  • Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

    Tri Huynh;Yaozong Gao;Jiayin Kang;Li Wang

  • Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

    Yanrong Guo;Yaozong Gao;Dinggang Shen

  • Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks.

    Dong Nie;Xiaohuan Cao;Yaozong Gao;Li Wang

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

    Li Wang;Yaozong Gao;Feng Shi;Gang Li

  • ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation

    Dong Nie;Yaozong Gao;Li Wang;Dinggang Shen

  • 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

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

    Liang Sun;Zhanhao Mo;Fuhua Yan;Liming Xia

  • Detecting Anatomical Landmarks for Fast Alzheimer’s Disease Diagnosis

    Jun Zhang;Yue Gao;Yaozong Gao;Brent C. Munsell

  • Representation learning: a unified deep learning framework for automatic prostate MR segmentation.

    Shu Liao;Yaozong Gao;Aytekin Oto;Dinggang Shen

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

    Li Wang;Feng Shi;Gang Li;Yaozong Gao

  • Abnormal Lung Quantification in Chest CT Images of COVID-19 Patients with Deep Learning and its Application to Severity Prediction.

    Fei Shan;Yaozong Gao;Jun Wang;Weiya Shi

  • Unsupervised deep feature learning for deformable registration of MR brain images

    Guorong Wu;Minjeong Kim;Qian Wang;Yaozong Gao

  • Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images.

    Xiaoming Liu;Quan Yuan;Yaozong Gao;Kelei He

  • Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images.

    Jun Zhang;Mingxia Liu;Le An;Yaozong Gao

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Feng Shi
Feng Shi United Imaging Intelligence (China)
Guorong Wu
Guorong Wu University of North Carolina at Chapel Hill
Weili Lin
Weili Lin University of North Carolina at Chapel Hill
Qian Wang
Qian Wang Shanghai Jiao Tong University
Gang Li
Gang Li University of North Carolina at Chapel Hill
Dong Nie
Dong Nie University of North Carolina at Chapel Hill
Yinghuan Shi
Yinghuan Shi Nanjing University
Daoqiang Zhang
Daoqiang Zhang Nanjing University of Aeronautics and Astronautics
Ligang Wu
Ligang Wu Harbin Institute of Technology

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