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Engineering and Technology
USA
2026

D-Index & Metrics

Engineering and Technology

D-Index
105
Citations
37543
World Ranking
112
National Ranking
45

Medicine

D-Index
105
Citations
36879
World Ranking
6817
National Ranking
3599

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in United States Leader Award
  • 2025 - Research.com Engineering and Technology in United States Leader Award
  • 2000 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

Kunio Doi is affiliated with the University of Chicago in the United States. Their research spans multiple scientific fields, with a focus on molecular biology, ecology, atmospheric science, plant science, and microbiology. Their work covers a variety of main topics including bacteriophages and microbial interactions, genomics and phylogenetic studies, microbial infections and disease research, pulsars and gravitational waves research, cryospheric studies and observations, enzyme production and characterization, and Arctic and Antarctic ice dynamics.

Doi's recent papers demonstrate a broad range of research areas and publication venues. Notable works include:

  • The status of KAGRA underground cryogenic gravitational wave telescope (2020, Journal of Physics Conference Series)
  • Development of a new gene expression vector for Thermus thermophilus using a silica-inducible promoter (2020, Microbial Cell Factories)
  • Freshening of Antarctic Bottom Water Off Cape Darnley, East Antarctica (2020, Journal of Geophysical Research Oceans)
  • GNSS Observations of GIA-Induced Crustal Deformation in Lützow-Holm Bay, East Antarctica (2021, Geophysical Research Letters)
  • Characterization and Genome Structure of Virulent Phage EspM4VN to Control Enterobacter sp. M4 Isolated From Plant Soft Rot (2020, Frontiers in Microbiology)

Doi frequently collaborates with several researchers, including Yasuhiro Fujino, Yasuaki Hiromasa, T. Akutsu, Masaki Ando, and A. Araya. These collaborations have resulted in multiple joint publications.

Their publications have appeared predominantly in journals such as Microbiology Resource Announcements, International Dairy Journal, Journal of Physics Conference Series, Microbial Cell Factories, and Geophysical Research Letters.

In 2000, Kunio Doi was recognized as a Fellow of the Indian National Academy of Engineering (INAE).

Best Publications

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

    Kunio Doi

  • 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

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

    H. Fujita;D.-Y. Tsai;T. Itoh;K. Doi

  • 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

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

    K Doi

  • 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

  • Computerized Detection of Pulmonary Nodules on CT Scans

    Samuel G. Armato;Maryellen L. Giger;Catherine J. Moran;James T. Blackburn

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

    Qiang Li;Shusuke Sone;Kunio Doi

  • 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

  • Improving breast cancer diagnosis with computer-aided diagnosis

    Yulei Jiang;Robert M. Nishikawa;Robert A. Schmidt;Charles E. Metz

  • Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields.

    Maryellen Lissak Giger;Kunio Doi;Heber MacMahon

  • 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

  • Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings

    Feng Li;Shusuke Sone;Hiroyuki Abe;Heber Macmahon

  • Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology

    Kunio Doi

  • Malignant and benign clustered microcalcifications: automated feature analysis and classification.

    Y Jiang;R M Nishikawa;D E Wolverton;C E Metz

  • Computer-aided diagnosis in radiology: potential and pitfalls

    Kunio Doi;Heber MacMahon;Shigehiko Katsuragawa;Robert M Nishikawa

  • Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program

    Samuel G. Armato;Feng Li;Maryellen L. Giger;Heber MacMahon

  • Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images.

    Fang-Fang Yin;Maryellen L. Giger;Kunio Doi;Charles E. Metz

  • Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.

    T Kobayashi;X W Xu;H MacMahon;C E Metz

  • Digital image subtraction of temporally sequential chest images for detection of interval change

    Akiko Kano;Kunio Doi;Heber MacMahon;Dayne D. Hassell

  • Automatic detection of abnormalities in chest radiographs using local texture analysis

    B. van Ginneken;S. Katsuragawa;B.M. ter Haar Romeny;Kunio Doi

Frequent Co-Authors

Maryellen L. Giger
Maryellen L. Giger University of Chicago
Heber MacMahon
Heber MacMahon University of Chicago
Qiang Li
Qiang Li Brookhaven National Laboratory
Robert M. Nishikawa
Robert M. Nishikawa University of Pittsburgh
Charles E. Metz
Charles E. Metz University of Chicago
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Kenji Suzuki
Kenji Suzuki Tokyo Institute of Technology
Hiroshi Fujita
Hiroshi Fujita Gifu University
Tomotari Mitsuoka
Tomotari Mitsuoka University of Tokyo
Takashi Onodera
Takashi Onodera University of Tokyo

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