World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4318
World Ranking
12734
National Ranking
1570

Yaoqin Xie 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 Yaoqin Xie 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: 246 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Yaoqin Xie 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 Yaoqin Xie 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Yaoqin Xie is affiliated with the Chinese Academy of Sciences in China. Their research spans multiple fields particularly focused on Medicine, Computer Science, and Engineering, with a significant concentration in subfields such as Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Computer Vision and Pattern Recognition, Radiation, and Artificial Intelligence.

The scientist's work predominantly covers topics related to Radiomics and Machine Learning in Medical Imaging, Medical Imaging Techniques and Applications, Advanced Radiotherapy Techniques, AI in cancer detection, Medical Imaging and Analysis, Advanced X-ray and CT Imaging, and Medical Image Segmentation Techniques.

Yaoqin Xie has a record of publications in various journals and conference venues, including:

  • arXiv (Cornell University)
  • Bioengineering
  • Computers in Biology and Medicine
  • Medical Image Analysis
  • IEEE Journal of Biomedical and Health Informatics

The following recent papers illustrate the scope of their research:

  • Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy, 2020, Quantitative Imaging in Medicine and Surgery
  • Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning, 2021, JAMA Network Open
  • Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network, 2021, Medical Image Analysis
  • Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study, 2021, The Lancet Digital Health
  • Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images, 2020, BioMed Research International

Coauthorship patterns highlight frequent collaboration with researchers such as Xiaokun Liang, Wenjian Qin, Chulong Zhang, Jingjing Dai, and Wenfeng He, with the highest number of joint publications with Xiaokun Liang.

Best Publications

  • A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

    Zhicheng Zhang;Xiaokun Liang;Xu Dong;Yaoqin Xie

  • Scatter correction for cone-beam CT in radiation therapy.

    Lei Zhu;Yaoqin Xie;Jing Wang;Lei Xing

  • Intrafractional motion of the prostate during hypofractionated radiotherapy.

    Yaoqin Xie;David Djajaputra;Christopher R. King;Sabbir Hossain

  • Objective assessment of deformable image registration in radiotherapy: A multi-institution study

    Rojano Kashani;Martina Hub;James M. Balter;Marc L. Kessler

  • A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

    Lian Zou;Shaode Yu;Shaode Yu;Tiebao Meng;Zhicheng Zhang

  • Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided Radiotherapy

    Wen Li;Yafen Li;Wenjian Qin;Xiaokun Liang

  • Breast mass lesion classification in mammograms by transfer learning

    Fan Jiang;Hui Liu;Shaode Yu;Yaoqin Xie

  • A Feasibility of Respiration Prediction Based on Deep Bi-LSTM for Real-Time Tumor Tracking

    Ran Wang;Xiaokun Liang;Xuanyu Zhu;Yaoqin Xie

  • Auto-propagation of contours for adaptive prostate radiation therapy.

    Ming Chao;Yaoqin Xie;Lei Xing

  • Evaluation of various speckle reduction filters on medical ultrasound images

    Shibin Wu;Qingsong Zhu;Yaoqin Xie

  • Robust Segmentation of Intima–Media Borders With Different Morphologies and Dynamics During the Cardiac Cycle

    Shen Zhao;Zhifan Gao;Heye Zhang;Yaoqin Xie

  • Feature‐based rectal contour propagation from planning CT to cone beam CT

    Yaoqin Xie;Ming Chao;Percy Lee;Lei Xing

  • A Novel Wearable Electrocardiogram Classification System Using Convolutional Neural Networks and Active Learning

    Yufa Xia;Yaoqin Xie

  • Iterative image-domain ring artifact removal in cone-beam CT

    Xiaokun Liang;Zhicheng Zhang;Tianye Niu;Shaode Yu

  • Learning based alpha matting using support vector regression

    Zhanpeng Zhang;Qingsong Zhu;Yaoqin Xie

  • A shallow convolutional neural network for blind image sharpness assessment.

    Shaode Yu;Shibin Wu;Lei Wang;Fan Jiang

  • Transferring deep neural networks for the differentiation of mammographic breast lesions

    ShaoDe Yu;LingLing Liu;ZhaoYang Wang;GuangZhe Dai

  • Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network.

    Xiaokun Liang;Na Li;Zhicheng Zhang;Jing Xiong

  • Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method

    Guanglei Zhang;Fei Liu;Jie Liu;Jianwen Luo

  • A Novel Recursive Bayesian Learning-Based Method for the Efficient and Accurate Segmentation of Video With Dynamic Background

    Qingsong Zhu;Zhan Song;Yaoqin Xie;Lei Wang

  • Using Edge-Preserving Algorithm with Non-local Mean for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT

    Wei Zhao;Tianye Niu;Lei Xing;Yaoqin Xie

  • TU‐C‐M100J‐03: Objective Assessment of Deformable Image Registration in Radiotherapy — a Multi‐Institution Study

    R Kashani;J Balter;M Kessler;M Hub

Frequent Co-Authors

Lei Xing
Lei Xing Stanford University
Xizhang Wang
Xizhang Wang Nanjing University
Tianmiao Wang
Tianmiao Wang Beihang University
Lei Wang
Lei Wang University of California, San Francisco
Ling Shao
Ling Shao Terminus International
Song Gao
Song Gao University of Wisconsin–Madison
Lei Wang
Lei Wang University of Wollongong
Shu-Hong Yu
Shu-Hong Yu University of Science and Technology of China
Yong Yang
Yong Yang Chinese Academy of Sciences
Sarang Joshi
Sarang Joshi University of Utah

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