World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
8150
World Ranking
7211
National Ranking
955

Dong Liang 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 Liang 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: 297 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Dong Liang 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 Liang 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Dong Liang is affiliated with the Chinese Academy of Sciences in China. Their research primarily focuses on the fields of medicine and engineering, with a significant emphasis on radiology, nuclear medicine, and imaging. The subfields most associated with their work include computer vision and pattern recognition, biomedical engineering, radiation, and computational mechanics.

The main topics covered in Dong Liang's research include medical imaging techniques and applications, advanced MRI techniques and applications, advanced X-ray and CT imaging, radiomics and machine learning in medical imaging, sparse and compressive sensing techniques, advanced neuroimaging techniques and applications, and medical image segmentation techniques.

Dong Liang has a substantial body of published works, with frequent publication venues highlighting their contributions. These venues include arXiv (Cornell University), Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition, IEEE Transactions on Medical Imaging, IEEE Transactions on Radiation and Plasma Medical Sciences, and Quantitative Imaging in Medicine and Surgery.

Among a selection of recent papers authored or co-authored by Dong Liang are:

  • Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks, 2020, IEEE Signal Processing Magazine
  • DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution, 2020, Magnetic Resonance Imaging
  • DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network, 2020, IEEE Transactions on Radiation and Plasma Medical Sciences
  • Domain generalization on medical imaging classification using episodic training with task augmentation, 2021, Computers in Biology and Medicine
  • CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging, 2020, IEEE Transactions on Computational Imaging

Collaborations have been an important part of their research output. Frequent co-authors include Hairong Zheng, Yanjie Zhu, Zhuo-Xu Cui, Zhanli Hu, and Jing Cheng.

Best Publications

  • Accelerating magnetic resonance imaging via deep learning

    Shanshan Wang;Zhenghang Su;Leslie Ying;Xi Peng

  • Accelerating SENSE using compressed sensing.

    Dong Liang;Bo Liu;Bo Liu;JiunJie Wang;Leslie Ying

  • Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks

    Dong Liang;Jing Cheng;Ziwen Ke;Leslie Ying

  • Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region.

    Qiuchang Sun;Xiaona Lin;Yuanshen Zhao;Ling Li

  • DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

    Shanshan Wang;Huitao Cheng;Leslie Ying;Taohui Xiao

  • A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification.

    Jie Zhang;Bowen Zheng;Ang Gao;Xin Feng

  • DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training

    Shanshan Wang;Ziwen Ke;Huitao Cheng;Sen Jia

  • Motion Tracking of the Carotid Artery Wall From Ultrasound Image Sequences: a Nonlinear State-Space Approach

    Zhifan Gao;Yanjie Li;Yuanyuan Sun;Jiayuan Yang

  • Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery

    Qiegen Liu;Shanshan Wang;Leslie Ying;Xi Peng

  • k-t ISD: Dynamic cardiac MR imaging using compressed sensing with iterative support detection

    Dong Liang;Edward V R DiBella;Rong Rong Chen;Leslie Ying

  • Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction

    Yuchou Chang;Dong Liang;Leslie Ying

  • Sensitivity encoding reconstruction with nonlocal total variation regularization.

    Dong Liang;Haifeng Wang;Yuchou Chang;Leslie Ying

  • A facial expression recognition system based on supervised locally linear embedding

    Dong Liang;Jie Yang;Zhonglong Zheng;Yuchou Chang

  • Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

    Congbo Cai;Chao Wang;Yiqing Zeng;Shuhui Cai

  • Compressed-sensing photoacoustic computed tomography in vivo with partially known support

    Jing Meng;Lihong V. Wang;Leslie Ying;Dong Liang

  • Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.

    Zhanli Hu;Changhui Jiang;Fengyi Sun;Qiyang Zhang

  • Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma.

    Zhi-Cheng Li;Hongmin Bai;Qiuchang Sun;Yuanshen Zhao

  • A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI

    Ukash Nakarmi;Yanhua Wang;Jingyuan Lyu;Dong Liang

  • DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network

    Zhanli Hu;Hengzhi Xue;Qiyang Zhang;Juan Gao

  • Highly Undersampled Magnetic Resonance Image Reconstruction Using Two-Level Bregman Method With Dictionary Updating

    Qiegen Liu;Shanshan Wang;Kun Yang;Jianhua Luo

  • DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

    Shanshan Wang;Huitao Cheng;Leslie Ying;Taohui Xiao

Frequent Co-Authors

Hairong Zheng
Hairong Zheng Chinese Academy of Sciences
Xin Liu
Xin Liu Chinese Academy of Sciences
Leslie Ying
Leslie Ying University at Buffalo, State University of New York
Xi Peng
Xi Peng Sichuan University
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Yuan-Ting Zhang
Yuan-Ting Zhang City University of Hong Kong
Qiang He
Qiang He Swinburne University of Technology
Edmund Y. Lam
Edmund Y. Lam University of Hong Kong
Xinghao Ding
Xinghao Ding Xiamen University
Jie Yang
Jie Yang RMIT University

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