World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
9431
World Ranking
4894
National Ranking
2274

Mingxia 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 Mingxia 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: 153 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Mingxia 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 Mingxia 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Mingxia Liu is affiliated with the University of North Carolina at Chapel Hill in the United States. Their research primarily focuses on applications within medicine and neuroscience, with significant contributions intersecting cognitive neuroscience, radiology, nuclear medicine and imaging, artificial intelligence, computer vision and pattern recognition, and neurology.

The scientist's work covers several specialized topics, including functional brain connectivity studies, advanced MRI techniques and applications, advanced neuroimaging techniques and applications, EEG and brain-computer interfaces, brain tumor detection and classification, radiomics and machine learning in medical imaging, as well as domain adaptation and few-shot learning.

Recent publications by Mingxia Liu include:

  • Federated learning for medical image analysis: A survey, 2024, published in Pattern Recognition
  • A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis, 2020, published in Frontiers in Neuroscience
  • A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity, 2021, published in IEEE Transactions on Medical Imaging
  • Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification, 2021, published in Medical Image Analysis
  • Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data, 2021, published in IEEE Transactions on Pattern Analysis and Machine Intelligence

Mingxia Liu frequently collaborates with a group of coauthors, including Dinggang Shen, Pew-Thian Yap, Lishan Qiao, Daoqiang Zhang, and Yuqi Fang.

Their publications have appeared predominantly in venues such as:

  • UNC Libraries
  • arXiv (Cornell University)
  • Medical Image Analysis
  • Lecture Notes in Computer Science
  • SSRN Electronic Journal

Best Publications

  • Domain Adaptation for Medical Image Analysis: A Survey

    Hao Guan;Mingxia Liu

  • Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI

    Chunfeng Lian;Mingxia Liu;Jun Zhang;Dinggang Shen

  • Landmark-based deep multi-instance learning for brain disease diagnosis

    Mingxia Liu;Jun Zhang;Ehsan Adeli;Dinggang Shen;Dinggang Shen

  • Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis

    Mingxia Liu;Jun Zhang;Ehsan Adeli;Dinggang Shen

  • A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.

    Li Zhang;Li Zhang;Mingliang Wang;Mingxia Liu;Daoqiang Zhang

  • Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment

    Mingxia Liu;Daoqiang Zhang;Dinggang Shen

  • Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data

    Tao Zhou;Mingxia Liu;Kim-Han Thung;Dinggang Shen

  • Domain Transfer Learning for MCI Conversion Prediction

    Bo Cheng;Mingxia Liu;Daoqiang Zhang;Brent C. Munsell

  • Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

    Jun Zhang;Mingxia Liu;Dinggang Shen

  • Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis.

    Yongsheng Pan;Mingxia Liu;Chunfeng Lian;Tao Zhou

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

    Jun Zhang;Mingxia Liu;Le An;Yaozong Gao

  • Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease

    Biao Jie;Biao Jie;Mingxia Liu;Dinggang Shen;Dinggang Shen

  • Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis

    Mingxia Liu;Daoqiang Zhang;Ehsan Adeli;Dinggang Shen

  • Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

    Mingxia Liu;Jun Zhang;Dong Nie;Pew-Thian Yap

  • Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation

    Mingliang Wang;Daoqiang Zhang;Jiashuang Huang;Pew-Thian Yap

  • A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity

    Dongren Yao;Jing Sui;Mingliang Wang;Erkun Yang

  • NLH: A Blind Pixel-Level Non-Local Method for Real-World Image Denoising

    Yingkun Hou;Jun Xu;Mingxia Liu;Guanghai Liu

  • View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data

    Mingxia Liu;Jun Zhang;Pew Thian Yap;Dinggang Shen;Dinggang Shen

  • Two-Stage Cost-Sensitive Learning for Software Defect Prediction

    Mingxia Liu;Linsong Miao;Daoqiang Zhang

  • Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network

    Mingliang Wang;Chunfeng Lian;Dongren Yao;Daoqiang Zhang

  • Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis.

    Yu Zhang;Yu Zhang;Han Zhang;Xiaobo Chen;Mingxia Liu

  • Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images.

    Chunfeng Lian;Jun Zhang;Mingxia Liu;Xiaopeng Zong

  • View‐centralized multi‐atlas classification for Alzheimer's disease diagnosis

    Mingxia Liu;Daoqiang Zhang;Dinggang Shen;Alzheimer's Disease Neuroimaging Initiative

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Daoqiang Zhang
Daoqiang Zhang Nanjing University of Aeronautics and Astronautics
Pew Thian Yap
Pew Thian Yap University of North Carolina at Chapel Hill
Weili Lin
Weili Lin University of North Carolina at Chapel Hill
Ehsan Adeli
Ehsan Adeli Stanford University
Feng Shi
Feng Shi United Imaging Intelligence (China)
Yong Xia
Yong Xia Northwestern Polytechnical University
Jing Sui
Jing Sui Beijing Normal University
Han Zhang
Han Zhang ShanghaiTech University
Songcan Chen
Songcan Chen Nanjing University of Aeronautics and Astronautics

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