World's Best Scientists 2026 revealed!
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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
57
Citations
11687
World Ranking
3879
National Ranking
37

Kenji Suzuki 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 Kenji Suzuki 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: 246 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Kenji Suzuki 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 Kenji Suzuki 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.

Research.com Recognitions

  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Kenji Suzuki is affiliated with the Tokyo Institute of Technology in Japan and has contributed extensively to the field of medicine, with a focus on medical imaging and artificial intelligence applications. Their research intersects multiple subfields such as Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Surgery, Artificial Intelligence, and Computer Vision and Pattern Recognition.

The scientist has published numerous papers in prominent venues, featuring topics related to radiomics, machine learning, and AI-driven diagnosis methods in healthcare. Notable recent papers include:

  • AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging (2022, Medical Physics)
  • Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring (2020, IEEE Transactions on Industrial Informatics)
  • Generation of 3D lacrimal gland organoids from human pluripotent stem cells (2022, Nature)
  • Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing (2024, BJR|Artificial Intelligence)
  • Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray (2021, BMC Urology)

Their frequent co-authors include Kazuya Takamochi, Ze Jin, Takeshi Matsunaga, Itsuo Kumazawa, and Wahyu Rahmaniar, reflecting active collaboration across various related research areas.

Kenji Suzuki has published in several recurrent venues, such as:

  • IEEE Access
  • arXiv (Cornell University)
  • BJR|Artificial Intelligence
  • Oncogene
  • Medical Physics

The main topics covered in their research consist of:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Lung Cancer Diagnosis and Treatment
  • Advanced X-ray and CT Imaging
  • Artificial Intelligence in Healthcare and Education
  • Advanced Neural Network Applications
  • Privacy-Preserving Technologies in Data

The breadth of their work highlights a multidisciplinary approach, emphasizing the integration of artificial intelligence within medical diagnostic and therapeutic contexts.

Best Publications

  • Overview of deep learning in medical imaging

    Kenji Suzuki;Kenji Suzuki

  • Linear-time connected-component labeling based on sequential local operations

    Kenji Suzuki;Isao Horiba;Noboru Sugie

  • Fast connected-component labeling

    Lifeng He;Yuyan Chao;Kenji Suzuki;Kesheng Wu

  • Optimizing two-pass connected-component labeling algorithms

    Kesheng Wu;Ekow Otoo;Kenji Suzuki

  • Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.

    Kenji Suzuki;Samuel G. Armato;Feng Li;Shusuke Sone

  • Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

    Ayman El-Baz;Garth M. Beache;Georgy L. Gimel'farb;Kenji Suzuki

  • Artificial Neural Networks - Methodological Advances and Biomedical Applications

    Kenji Suzuki

  • A Run-Based Two-Scan Labeling Algorithm

    Lifeng He;Yuyan Chao;K. Suzuki

  • Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

    K. Suzuki;H. Abe;H. MacMahon;K. Doi

  • Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network

    K. Suzuki;Feng Li;S. Sone;K. Doi

  • Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening

    Hidetaka Arimura;Shigehiko Katsuragawa;Kenji Suzuki;Feng Li

  • Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography

    Yoshikazu Uchiyama;Shigehiko Katsuragawa;Hiroyuki Abe;Junji Shiraishi

  • Computer-Aided Diagnosis

    Maryellen L. Giger;Kenji Suzuki

  • Artificial Neural Networks : Architectures and Applications

    Kenji Suzuki

  • Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification

    Nima Tajbakhsh;Kenji Suzuki

  • Neural edge enhancer for supervised edge enhancement from noisy images

    K. Suzuki;I. Horiba;N. Sugie

  • False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network

    Kenji Suzuki;Junji Shiraishi;Hiroyuki Abe;Heber MacMahon

  • Massive training artificial neural network (mtann) for detecting abnormalities in medical images

    Kenji Suzuki;Kunio Doi

  • Image modification and detection using massive training artificial neural networks (MTANN)

    Kenji Suzuki;Kunio Doi

  • Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

    Kenji Suzuki;Ryan Kohlbrenner;Mark L. Epstein;Ademola M. Obajuluwa

  • 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

Kunio Doi
Kunio Doi University of Chicago
Qiang Li
Qiang Li Brookhaven National Laboratory
Heber MacMahon
Heber MacMahon University of Chicago
Maryellen L. Giger
Maryellen L. Giger University of Chicago
Dinggang Shen
Dinggang Shen ShanghaiTech University
Pingkun Yan
Pingkun Yan Rensselaer Polytechnic Institute
Xiaochuan Pan
Xiaochuan Pan University of Chicago
Don C. Rockey
Don C. Rockey Medical University of South Carolina
Kesheng Wu
Kesheng Wu Lawrence Berkeley National Laboratory
Georgy Gimel'farb
Georgy Gimel'farb University of Auckland

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