World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
11016
World Ranking
3889
National Ranking
1841

Eric Chang 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 Eric Chang 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: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Eric Chang 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 Eric Chang 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

Eric Chang is affiliated with Microsoft in the United States. Their research primarily spans the fields of Computer Science and Medicine, with significant contributions in subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Surgery, and Biophysics.

Their work addresses several main topics including AI in cancer detection, digital imaging for blood diseases, generative adversarial networks and image synthesis, radiomics and machine learning in medical imaging, medical image segmentation techniques, image retrieval and classification techniques, and cell image analysis techniques.

Chang has collaborated frequently with several researchers throughout their career. Notable frequent coauthors include Yan Xu, Yubo Fan, Maode Lai, Kailu Li, and Bingzheng Wei.

Eric Chang's publications appear in various scientific venues, with a strong presence in both interdisciplinary and technical journals. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • Frontiers of Medicine
  • IEEE Transactions on Medical Imaging
  • Scientific Reports

Key recent papers by Chang include:

  • Deep learning in digital pathology image analysis: a survey (2020, Frontiers of Medicine)
  • ANHIR: Automatic Non-Rigid Histological Image Registration Challenge (2020, IEEE Transactions on Medical Imaging)
  • Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021, arXiv [Cornell University])
  • MRI Cross-Modality Image-to-Image Translation (2020, Scientific Reports)
  • Weakly supervised histopathology image segmentation with self-attention (2023, Medical Image Analysis)

Best Publications

  • Forecasting Fine-Grained Air Quality Based on Big Data

    Yu Zheng;Xiuwen Yi;Ming Li;Ruiyuan Li

  • Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features.

    Yan Xu;Yan Xu;Zhipeng Jia;Zhipeng Jia;Liang-Bo Wang;Liang-Bo Wang;Yuqing Ai;Yuqing Ai

  • Deep learning of feature representation with multiple instance learning for medical image analysis

    Yan Xu;Tao Mo;Qiwei Feng;Peilin Zhong

  • Location mapping for key-point based services

    Eric Chang;Kong-Kat Wong;Difei Tang

  • MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion Mask

    Shengyu Zhao;Yilun Sheng;Yue Dong;Eric I-Chao Chang

  • Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

    Mitko Veta;Yujing J. Heng;Nikolas Stathonikos;Babak Ehteshami Bejnordi

  • Weakly supervised histopathology cancer image segmentation and classification

    Yan Xu;Yan Xu;Jun-Yan Zhu;Eric I-Chao Chang;Maode Lai

  • Inferring gas consumption and pollution emission of vehicles throughout a city

    Jingbo Shang;Yu Zheng;Wenzhu Tong;Eric Chang

  • Diagnosing New York city's noises with ubiquitous data

    Yu Zheng;Tong Liu;Yilun Wang;Yanmin Zhu

  • Constrained Deep Weak Supervision for Histopathology Image Segmentation

    Zhipeng Jia;Xingyi Huang;Eric I-Chao Chang;Yan Xu

  • Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network

    Shengyu Zhao;Tingfung Lau;Ji Luo;Eric I-Chao Chang

  • Personal Media Landscapes in Mixed Reality

    Darren K. Edge;Eric Chang;Kyungmin Min

  • Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network.

    Shengyu Zhao;Tingfung Lau;Ji Luo;Eric I-Chao Chang

  • Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning

    Jun-Yan Zhu;Jiajun Wu;Yan Xu;Eric Chang

  • Emotion Detection from Speech to Enrich Multimedia Content

    Feng Yu;Eric Chang;Yingqing Xu;Heung-Yeung Shum

  • Natural language speech recognition using slot semantic confidence scores related to their word recognition confidence scores

    Eric I Chao Chang;Eric G. Jackson

  • Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation

    Yan Xu;Zhipeng Jia;Yuqing Ai;Fang Zhang

  • Voice conversion with smoothed GMM and MAP adaptation.

    Yining Chen;Min Chu;Eric Chang;Jia Liu

  • Gland Instance Segmentation Using Deep Multichannel Neural Networks

    Yan Xu;Yang Li;Yipei Wang;Mingyuan Liu

  • Deep learning in digital pathology image analysis: a survey

    Shujian Deng;Xin Zhang;Wen Yan;Eric I-Chao Chang

  • Using Genetic Algorithms to Improve Pattern Classification Performance

    Eric I. Chang;Richard P Lippmann

  • Unsupervised object class discovery via saliency-guided multiple class learning

    Jun-Yan Zhu;Jiajun Wu;Yichen Wei;Eric Chang

Frequent Co-Authors

Zhuowen Tu
Zhuowen Tu University of California, San Diego
Jun'ichi Tsujii
Jun'ichi Tsujii University of Manchester
Yubo Fan
Yubo Fan Beihang University
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University
Yu Zheng
Yu Zheng Jingdong (China)
Jiajun Wu
Jiajun Wu Stanford University
Frank Seide
Frank Seide Microsoft (United States)
Wei-Ying Ma
Wei-Ying Ma Tsinghua University
Jian-Tao Sun
Jian-Tao Sun Microsoft (United States)

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