World's Best Scientists 2026 revealed!
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Engineering and Technology
USA
2026

D-Index & Metrics

Medicine

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

Engineering and Technology

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

Kunio Doi publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Kunio Doi sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 638 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Kunio Doi D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Kunio Doi sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 105 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

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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