H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 44 Citations 7,943 165 World Ranking 3665 National Ranking 47

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Kenji Suzuki focuses on Artificial neural network, Artificial intelligence, Radiology, Computer-aided diagnosis and Medical imaging. The Artificial neural network study combines topics in areas such as Feature extraction, Deep learning and Convolutional neural network. His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Pattern recognition.

His Radiology study combines topics in areas such as Image processing, Cancer, Computer-aided and Nuclear medicine. His Computer-aided diagnosis research is multidisciplinary, incorporating perspectives in Image segmentation and Radiography. The various areas that he examines in his Medical imaging study include Segmentation and Computed tomography.

His most cited work include:

  • Linear-time connected-component labeling based on sequential local operations (367 citations)
  • Overview of deep learning in medical imaging (262 citations)
  • Overview of deep learning in medical imaging (262 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Computer vision, Artificial neural network, Pattern recognition and Radiology. His research on Artificial intelligence often connects related topics like Machine learning. His biological study spans a wide range of topics, including Deep learning, Convolutional neural network and Nodule.

Kenji Suzuki interconnects False positive paradox, Mammography and Reduction in the investigation of issues within Pattern recognition. His Radiology research includes elements of Computer aided detection and Lung. His research in Image intersects with topics in Algorithm and Connected component.

He most often published in these fields:

  • Artificial intelligence (53.76%)
  • Computer vision (24.81%)
  • Artificial neural network (25.94%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (53.76%)
  • Pattern recognition (23.31%)
  • Deep learning (6.02%)

In recent papers he was focusing on the following fields of study:

Kenji Suzuki mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Artificial neural network and Intermetallic. The Artificial intelligence study combines topics in areas such as Machine learning and Reduction. As a part of the same scientific family, he mostly works in the field of Pattern recognition, focusing on Filter and, on occasion, Sensitivity, Transfer of learning, Thoracic computed tomography, Region growing and Binary image.

His Artificial neural network research incorporates themes from Segmentation, Convolution and Ground-glass opacity. His Convolutional neural network research integrates issues from False positive paradox, Training set and Feature. The study incorporates disciplines such as Computer-aided diagnosis and Decision support system in addition to Medical imaging.

Between 2015 and 2021, his most popular works were:

  • Overview of deep learning in medical imaging (262 citations)
  • Overview of deep learning in medical imaging (262 citations)
  • Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification (85 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Kenji Suzuki focuses on Artificial intelligence, Deep learning, Convolutional neural network, Artificial neural network and Feature. His Artificial intelligence research is multidisciplinary, relying on both Data mining, Reduction, Computer vision, Condition monitoring and Pattern recognition. His Computer vision study incorporates themes from Outlier and Generalized Procrustes analysis.

Kenji Suzuki has researched Pattern recognition in several fields, including Selection, Binary number, Heuristic and Heuristic. His Deep learning research includes themes of Probability distribution, Recurrent neural network, Imaging phantom, Nuclear medicine and Entropy. His Artificial neural network research is included under the broader classification of Machine learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Kenji Suzuki;Isao Horiba;Noboru Sugie.
Computer Vision and Image Understanding (2003)

599 Citations

Fast connected-component labeling

Lifeng He;Yuyan Chao;Kenji Suzuki;Kesheng Wu.
Pattern Recognition (2009)

370 Citations

Overview of deep learning in medical imaging

Kenji Suzuki;Kenji Suzuki.
Radiological Physics and Technology (2017)

349 Citations

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.
Medical Physics (2003)

320 Citations

A Run-Based Two-Scan Labeling Algorithm

Lifeng He;Yuyan Chao;K. Suzuki.
IEEE Transactions on Image Processing (2008)

292 Citations

Optimizing two-pass connected-component labeling algorithms

Kesheng Wu;Ekow Otoo;Kenji Suzuki.
Pattern Analysis and Applications (2009)

287 Citations

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.
IEEE Transactions on Medical Imaging (2005)

262 Citations

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

Ayman El-Baz;Garth M. Beache;Georgy L. Gimel'farb;Kenji Suzuki.
International Journal of Biomedical Imaging (2013)

252 Citations

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.
IEEE Transactions on Medical Imaging (2006)

248 Citations

Computer-Aided Diagnosis

Maryellen L. Giger;Kenji Suzuki.
Biomedical Information Technology (2008)

220 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Kenji Suzuki

Kunio Doi

Kunio Doi

University of Chicago

Publications: 42

Ronald M. Summers

Ronald M. Summers

National Institutes of Health

Publications: 31

Maryellen L. Giger

Maryellen L. Giger

University of Chicago

Publications: 26

Hiroshi Fujita

Hiroshi Fujita

Gifu University

Publications: 20

Bin Zheng

Bin Zheng

University of Oklahoma

Publications: 18

Zhengrong Liang

Zhengrong Liang

Stony Brook University

Publications: 16

Wei Qian

Wei Qian

The University of Texas at El Paso

Publications: 14

Ayman El-Baz

Ayman El-Baz

University of Louisville

Publications: 13

Jianhua Yao

Jianhua Yao

Tencent (China)

Publications: 12

Ulas Bagci

Ulas Bagci

University of Central Florida

Publications: 12

Heber MacMahon

Heber MacMahon

University of Chicago

Publications: 12

Heang Ping Chan

Heang Ping Chan

University of Michigan–Ann Arbor

Publications: 11

Perry J. Pickhardt

Perry J. Pickhardt

University of Wisconsin–Madison

Publications: 11

Lubomir M. Hadjiiski

Lubomir M. Hadjiiski

University of Michigan–Ann Arbor

Publications: 10

Kesheng Wu

Kesheng Wu

Lawrence Berkeley National Laboratory

Publications: 10

Sameer Antani

Sameer Antani

National Institutes of Health

Publications: 10

Something went wrong. Please try again later.