World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
7624
World Ranking
5807
National Ranking
1626

Tonghe Wang publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Tonghe Wang sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 280 publications — 72nd percentile

72% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Tonghe Wang D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Tonghe Wang sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 44 D-Index — 42nd percentile

42% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Tonghe Wang is affiliated with the Memorial Sloan Kettering Cancer Center in the United States. Their research primarily focuses on the field of medicine with a specialized emphasis on radiology, nuclear medicine and imaging, biomedical engineering, radiation, computer vision and pattern recognition, as well as pulmonary and respiratory medicine.

Their work encompasses a range of topics including:

  • Medical Imaging Techniques and Applications
  • Advanced Radiotherapy Techniques
  • Radiomics and Machine Learning in Medical Imaging
  • Advanced X-ray and CT Imaging
  • Advanced Neural Network Applications
  • Medical Imaging and Analysis
  • Radiation Therapy and Dosimetry

Some of their recent publications are:

  • A review on medical imaging synthesis using deep learning and its clinical applications, 2020, Journal of Applied Clinical Medical Physics
  • Deep learning in medical image registration: a review, 2020, Physics in Medicine and Biology
  • A review of deep learning based methods for medical image multi-organ segmentation, 2021, Physica Medica
  • CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy, 2020, Medical Physics
  • 2D medical image synthesis using transformer-based denoising diffusion probabilistic model, 2023, Physics in Medicine and Biology

They have frequently published in venues such as:

  • Medical Physics
  • arXiv (Cornell University)
  • Physics in Medicine and Biology
  • Journal of Applied Clinical Medical Physics
  • Medical Imaging 2020: Physics of Medical Imaging

Frequent collaborators in their academic career include Xiaofeng Yang, Yang Lei, Walter J. Curran, Justin Roper, and Tian Liu. These co-authors have contributed significantly in partnership on multiple research projects.

Best Publications

  • Deep learning in medical image registration: a review.

    Yabo Fu;Yang Lei;Tonghe Wang;Walter J Curran

  • MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.

    Yang Lei;Joseph Harms;Tonghe Wang;Yingzi Liu

  • Automatic multiorgan segmentation in thorax CT images using U-net-GAN.

    Xue Dong;Yang Lei;Tonghe Wang;Matthew Thomas

  • Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography

    Joseph Harms;Yang Lei;Tonghe Wang;Rongxiao Zhang

  • A review on medical imaging synthesis using deep learning and its clinical applications.

    Tonghe Wang;Yang Lei;Yabo Fu;Jacob F. Wynne

  • A review of deep learning based methods for medical image multi-organ segmentation.

    Yabo Fu;Yang Lei;Tonghe Wang;Walter J. Curran

  • Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

    Bo Wang;Bo Wang;Yang Lei;Sibo Tian;Tonghe Wang

  • Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.

    Xue Dong;Yang Lei;Sibo Tian;Tonghe Wang

  • Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net

    Yang Lei;Sibo Tian;Xiuxiu He;Tonghe Wang

  • Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging.

    Xue Dong;Tonghe Wang;Yang Lei;Kristin Higgins

  • Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

    Xue Dong;Yang Lei;Tonghe Wang;Kristin Higgins

  • Male Pelvic Multi-Organ Segmentation Aided by CBCT-based Synthetic MRI

    Yang Lei;Tonghe Wang;Sibo Tian;Xue Dong

  • A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study

    Tonghe Wang;Yang Lei;Haipeng Tang;Zhuo He

  • CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.

    Yang Lei;Xue Dong;Zhen Tian;Yingzi Liu

  • Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks

    Yang Lei;Xue Dong;Tonghe Wang;Kristin Higgins

  • MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

    Yingzi Liu;Yang Lei;Yinan Wang;Tonghe Wang

  • LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.

    Yabo Fu;Yang Lei;Tonghe Wang;Kristin Higgins

  • Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN.

    Yang Lei;Xiuxiu He;Jincao Yao;Tonghe Wang

  • Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning.

    Yingzi Liu;Yang Lei;Yinan Wang;Ghazal Shafai-Erfani

  • MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method

    Yingzi Liu;Yang Lei;Tonghe Wang;Oluwatosin Kayode

  • 4D-CT deformable image registration using multiscale unsupervised deep learning.

    Yang Lei;Yabo Fu;Tonghe Wang;Yingzi Liu

  • Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.

    Tonghe Wang;Yang Lei;Yabo Fu;Walter J. Curran

  • Biomechanically constrained non-rigid MR-TRUS prostate registration using deep learning based 3D point cloud matching.

    Yabo Fu;Yang Lei;Tonghe Wang;Pretesh Patel

  • MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning

    Xiaofeng Yang;Tonghe Wang;Yang Lei;Kristin Higgins

  • CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy

    Yingzi Liu;Yang Lei;Yabo Fu;Tonghe Wang

  • Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning

    Yupei Zhang;Xiuxiu He;Zhen Tian;Jiwoong Jason Jeong

  • Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy.

    Qiulan Zeng;Yabo Fu;Zhen Tian;Yang Lei

  • Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

    Ghazal Shafai-Erfani;Tonghe Wang;Yang Lei;Sibo Tian

  • Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy.

    Xianjin Dai;Yang Lei;Tonghe Wang;Anees H Dhabaan

  • Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy.

    Tonghe Wang;Yang Lei;Zhen Tian;Xue Dong

  • Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.

    Yang Lei;Hui-Kuo Shu;Sibo Tian;Jiwoong Jason Jeong

  • Automated prostate segmentation of volumetric CT images using 3D deeply supervised dilated FCN

    Bo Wang;Bo Wang;Yang Lei;Tonghe Wang;Xue Dong

Frequent Co-Authors

Yang Lei
Yang Lei University of Nevada Reno
Walter J. Curran
Walter J. Curran Emory University

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