World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
11532
World Ranking
4338
National Ranking
2032

Pingkun Yan 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 Pingkun Yan 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: 210 publications — 50th percentile

50% of scientists in this discipline score the same or lower.

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

Pingkun Yan 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 Pingkun Yan 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Pingkun Yan is affiliated with Rensselaer Polytechnic Institute in the United States. Their research focuses on fields intersecting medicine and computer science, with significant contributions to radiology, nuclear medicine, imaging, computer vision, and artificial intelligence.

The scientist's work spans several subfields including:

  • Radiology, Nuclear Medicine and Imaging
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Biomedical Engineering
  • Pulmonary and Respiratory Medicine

Key research topics explored by Pingkun Yan include:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Medical Image Segmentation Techniques
  • Medical Imaging Techniques and Applications
  • Domain Adaptation and Few-Shot Learning
  • AI in cancer detection

The researcher has a strong publication record with several papers appearing in prominent journals. Recent notable papers include:

  • "Development of metaverse for intelligent healthcare," 2022, Nature Machine Intelligence
  • "Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction," 2020, IEEE Transactions on Medical Imaging
  • "Integrative analysis for COVID-19 patient outcome prediction," 2021, UNICA IRIS Institutional Research Information System (University of Cagliari)
  • "Cross-modal attention for multi-modal image registration," 2022, Medical Image Analysis
  • "Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study," 2020, Journal of Magnetic Resonance Imaging

Frequent co-authors collaborating with Pingkun Yan include:

  • Ge Wang
  • Xuanang Xu
  • Hanqing Chao
  • Mannudeep K. Kalra
  • Bradford J. Wood

Their research has been published in a variety of venues, with multiple papers appearing in:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • International Journal of Computer Assisted Radiology and Surgery
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Medical Image Analysis

Pingkun Yan has also contributed to book publications with Springer Science+Business Media, authoring editions of Machine Learning in Medical Imaging in 2020 and 2021.

Best Publications

  • Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

    Qingsong Yang;Pingkun Yan;Yanbo Zhang;Hengyong Yu

  • Deep learning in medical image registration: a survey

    Grant Haskins;Uwe Kruger;Pingkun Yan

  • Magnetic Resonance Imaging/Ultrasound Fusion Guided Prostate Biopsy Improves Cancer Detection Following Transrectal Ultrasound Biopsy and Correlates With Multiparametric Magnetic Resonance Imaging

    Peter A. Pinto;Paul H. Chung;Ardeshir R. Rastinehad;Angelo A. Baccala

  • Manifold Regularized Sparse NMF for Hyperspectral Unmixing

    Xiaoqiang Lu;Hao Wu;Yuan Yuan;Pingkun Yan

  • Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

    Qingsong Yang;Pingkun Yan;Yanbo Zhang;Hengyong Yu

  • Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

    Qikui Zhu;Bo Du;Pingkun Yan

  • Learning 4D action feature models for arbitrary view action recognition

    Pingkun Yan;S.M. Khan;M. Shah

  • Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction

    Xi Fang;Pingkun Yan

  • Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval

    Subrahmanyam Murala;Q. M. Jonathan Wu

  • Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

    Pingkun Yan;Xiaobo Zhou;Xiaobo Zhou;M. Shah;S.T.C. Wong

  • Deeply-supervised CNN for prostate segmentation

    Qikui Zhu;Bo Du;Baris Turkbey;Peter L. Choyke

  • Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos

    Xianbin Cao;Changxia Wu;Pingkun Yan;Xuelong Li

  • Saliency Detection by Multiple-Instance Learning

    Qi Wang;Yuan Yuan;Pingkun Yan;Xuelong Li

  • Fast fit-free analysis of fluorescence lifetime imaging via deep learning.

    Jason T Smith;Ruoyang Yao;Nattawut Sinsuebphon;Alena Rudkouskaya

  • Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography

    Hanqing Chao;Hongming Shan;Fatemeh Homayounieh;Ramandeep Singh

  • Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation

    Pingkun Yan;Sheng Xu;Baris Turkbey;Jochen Kruecker

  • D'Amico risk stratification correlates with degree of suspicion of prostate cancer on multiparametric magnetic resonance imaging.

    Ardeshir R Rastinehad;Angelo A Baccala;Paul H Chung;Juan M Proano

  • 3D Model based Object Class Detection in An Arbitrary View

    Pingkun Yan;S.M. Khan;M. Shah

  • MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection

    Jun Shi;Zheng Li;Shihui Ying;Chaofeng Wang

  • Single-image super-resolution via local learning

    Yi Tang;Pingkun Yan;Yuan Yuan;Xuelong Li

  • Learning deep similarity metric for 3D MR-TRUS image registration.

    Grant Haskins;Jochen Kruecker;Uwe Kruger;Sheng Xu

  • Machine learning in medical imaging

    Pingkun Yan;Kenji Suzuki;Fei Wang;Dinggang Shen

  • Editorial: machine learning in medical imaging

    Kenji Suzuki;Pingkun Yan;Fei Wang;Dinggang Shen

Frequent Co-Authors

Xuelong Li
Xuelong Li China Telecom (China)
Yuan Yuan
Yuan Yuan Huawei Technologies (China)
Bradford J. Wood
Bradford J. Wood National Institutes of Health
Baris Turkbey
Baris Turkbey National Institutes of Health
Peter L. Choyke
Peter L. Choyke National Institutes of Health
Ge Wang
Ge Wang Rensselaer Polytechnic Institute
Peter A. Pinto
Peter A. Pinto National Institutes of Health
Xavier Intes
Xavier Intes Rensselaer Polytechnic Institute
Uwe Kruger
Uwe Kruger Rensselaer Polytechnic Institute
Bo Du
Bo Du Wuhan University

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