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

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
Pingkun Yan
Pingkun Yan Rensselaer Polytechnic Institute
Dinggang Shen
Dinggang Shen ShanghaiTech University
Xiaochuan Pan
Xiaochuan Pan University of Chicago
Kesheng Wu
Kesheng Wu Lawrence Berkeley National Laboratory
Ayman El-Baz
Ayman El-Baz University of Louisville
Georgy Gimel'farb
Georgy Gimel'farb University of Auckland
Guorong Wu
Guorong Wu University of North Carolina at Chapel Hill
Daoqiang Zhang
Daoqiang Zhang Nanjing University of Aeronautics and Astronautics

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