D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 89 Citations 25,960 365 World Ranking 63 National Ranking 34
Medicine D-index 86 Citations 25,666 366 World Ranking 7116 National Ranking 3908

Research.com Recognitions

Awards & Achievements

2000 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Radiology
  • Internal medicine

Kunio Doi mainly investigates Radiology, Artificial intelligence, Computer-aided diagnosis, Radiography and Medical imaging. His Radiology research includes elements of Receiver operating characteristic analysis, Lung, Nuclear medicine and Receiver operating characteristic. He has researched Artificial intelligence in several fields, including Mammography, Computer vision and Pattern recognition.

In his research on the topic of Computer-aided diagnosis, Second opinion is strongly related with CAD. The concepts of his Radiography study are interwoven with issues in Tomography and Early detection. His study explores the link between Medical imaging and topics such as Digital radiography that cross with problems in Optics and Optical transfer function.

His most cited work include:

  • Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential (1039 citations)
  • A simple method for determining the modulation transfer function in digital radiography (570 citations)
  • Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. (499 citations)

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

His main research concerns Radiography, Radiology, Artificial intelligence, Computer-aided diagnosis and Computer vision. His Radiography study combines topics from a wide range of disciplines, such as Image quality, Nuclear medicine and Optics. Kunio Doi combines subjects such as Temporal subtraction, Computed radiography and Radiographic Image Enhancement with his study of Nuclear medicine.

Kunio Doi interconnects Differential diagnosis, Lung cancer, Lung and Receiver operating characteristic in the investigation of issues within Radiology. His Artificial intelligence research is multidisciplinary, relying on both Mammography and Pattern recognition. Kunio Doi focuses mostly in the field of Computer-aided diagnosis, narrowing it down to matters related to CAD and, in some cases, Second opinion.

He most often published in these fields:

  • Radiography (36.17%)
  • Radiology (35.05%)
  • Artificial intelligence (33.28%)

What were the highlights of his more recent work (between 2006-2021)?

  • Radiology (35.05%)
  • Computer-aided diagnosis (25.72%)
  • Artificial intelligence (33.28%)

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

His scientific interests lie mostly in Radiology, Computer-aided diagnosis, Artificial intelligence, Computer vision and Radiography. The study incorporates disciplines such as Differential diagnosis, Lung, Nuclear medicine and Receiver operating characteristic in addition to Radiology. His study in Computer-aided diagnosis is interdisciplinary in nature, drawing from both False positive paradox, Medical imaging, Presentation, CAD and Hemangioma.

His Artificial intelligence study combines topics in areas such as Mammography and Pattern recognition. His studies in Computer vision integrate themes in fields like Temporal subtraction and Input device. His Radiography research integrates issues from Thorax, Computer aided detection and Projection.

Between 2006 and 2021, his most popular works were:

  • Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential (1039 citations)
  • Quantitative Evaluation of Liver Function with Use of Gadoxetate Disodium–enhanced MR Imaging (130 citations)
  • Computerized Detection of Lung Nodules in Thin-Section CT Images by use of Selective Enhancement Filters and an Automated Rule-Based Classifier (118 citations)

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

  • Artificial intelligence
  • Radiology
  • Internal medicine

His primary areas of study are Radiology, Computer-aided diagnosis, Radiography, Artificial intelligence and Lung. His Radiology research is multidisciplinary, incorporating perspectives in Differential diagnosis, Nuclear medicine and Receiver operating characteristic. His Computer-aided diagnosis research incorporates elements of Medical imaging, Diagnostic accuracy, Breast imaging, CAD and Observer performance.

His biological study spans a wide range of topics, including Generalization, Cross-validation, Monte Carlo method and Econometrics. His Artificial intelligence research includes themes of Mammography, Computer vision and Pattern recognition. The various areas that Kunio Doi examines in his Lung study include False positive paradox, Lateral chest, Thorax, Lung cancer and Dual energy subtraction.

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

Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential

Kunio Doi.
Computerized Medical Imaging and Graphics (2007)

1426 Citations

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA

B. P. Abbott;R. Abbott;T. D. Abbott;M. R. Abernathy.
Living Reviews in Relativity (2018)

1005 Citations

A simple method for determining the modulation transfer function in digital radiography

H. Fujita;D.-Y. Tsai;T. Itoh;K. Doi.
IEEE Transactions on Medical Imaging (1992)

885 Citations

Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

Junji Shiraishi;Shigehiko Katsuragawa;Junpei Ikezoe;Tsuneo Matsumoto.
American Journal of Roentgenology (2000)

609 Citations

Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.

Yuzheng Wu;M. L. Giger;Kunio Doi;C. J. Vyborny.
Radiology (1993)

557 Citations

Current status and future potential of computer-aided diagnosis in medical imaging.

K Doi.
British Journal of Radiology (2005)

518 Citations

Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.

H P Chan;K Doi;C J Vyborny;R A Schmidt.
Investigative Radiology (1990)

511 Citations

Computerized Detection of Pulmonary Nodules on CT Scans

Samuel G. Armato;Maryellen L. Giger;Catherine J. Moran;James T. Blackburn.
Radiographics (1999)

509 Citations

Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.

Qiang Li;Shusuke Sone;Kunio Doi.
Medical Physics (2003)

497 Citations

Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography

Heang Ping Chan;Kunio Doi;Simranjit Galhotra;Carl J. Vyborny.
Medical Physics (1987)

492 Citations

Best Scientists Citing Kunio Doi

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Maryellen L. Giger

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Bin Zheng

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Hiroshi Fujita

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Heang Ping Chan

Heang Ping Chan

University of Michigan–Ann Arbor

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Kenji Suzuki

Kenji Suzuki

Tokyo Institute of Technology

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Lubomir M. Hadjiiski

Lubomir M. Hadjiiski

University of Michigan–Ann Arbor

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Berkman Sahiner

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United States Food and Drug Administration

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Rangaraj M. Rangayyan

Rangaraj M. Rangayyan

University of Calgary

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Robert M. Nishikawa

Robert M. Nishikawa

University of Pittsburgh

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Nico Karssemeijer

Nico Karssemeijer

Radboud University Nijmegen

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Wei Qian

Wei Qian

The University of Texas at El Paso

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Mark A. Helvie

Mark A. Helvie

University of Michigan–Ann Arbor

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Yongyi Yang

Yongyi Yang

Illinois Institute of Technology

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Joseph Y. Lo

Joseph Y. Lo

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Bram van Ginneken

Bram van Ginneken

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Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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