World's Best Scientists 2026 revealed!
Hiroshi Fujita

Hiroshi Fujita

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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
60
Citations
14801
World Ranking
3229
National Ranking
27

Hiroshi Fujita publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Hiroshi Fujita sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 622 publications — 97th percentile

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

The last bar groups every scientist with 991 publications or more.

Hiroshi Fujita D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Hiroshi Fujita sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Hiroshi Fujita is affiliated with Gifu University in Japan. Their research spans multiple disciplines including medicine, engineering, and business, management, and accounting, with a focus on the intersection of technology and healthcare.

Their work involves several main fields of study:

  • Medicine
  • Engineering
  • Business, Management and Accounting

Within these broader fields, Fujita's subfields of study include:

  • Management of Technology and Innovation
  • Biochemistry
  • Genetics
  • Mechanical Engineering
  • Aerospace Engineering

Their research topics highlight a focus on blood-related medical practices and emerging technologies, including:

  • Blood donation and transfusion practices
  • Blood transfusion and management
  • Hemoglobinopathies and related disorders
  • Neurogenetic and muscular disorders research
  • Travel-related health issues
  • Trauma, hemostasis, coagulopathy, resuscitation
  • UAV applications and optimization

Frequent co-authors collaborating with Fujita include:

  • Koki Yakushiji
  • Fumiatsu Yakushiji
  • Mikio Murata
  • Asashi Tanaka
  • Makoto Okuda

Fujita's recent published papers reflect the application of unmanned aerial vehicles (UAVs) in medical contexts as well as trauma-related clinical studies:

  • Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan, 2020, published in Drones
  • The Quality of Blood is not Affected by Drone Transport: An Evidential Study of the Unmanned Aerial Vehicle Conveyance of Transfusion Material in Japan, 2020, published in Drones
  • Quality Control of Red Blood Cell Solutions for Transfusion Transported via Drone Flight to a Remote Island, 2021, published in Drones
  • Effects of in-house cryoprecipitate on transfusion usage and mortality in patients with multiple trauma with severe traumatic brain injury: a retrospective cohort study, 2020, published in PubMed
  • Newborn screening for spinal muscular atrophy in Osaka -challenges in a Japanese pilot study-, 2023, published in Brain and Development

Their frequent publication venues indicate specialization and interest in transfusion medicine and technological applications in healthcare:

  • Japanese Journal of Transfusion and Cell Therapy
  • Hematology & Transfusion International Journal
  • Drones
  • Vox Sanguinis
  • Brain and Development

Best Publications

  • 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

  • Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique

    Yongbum Lee;T. Hara;H. Fujita;S. Itoh

  • Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

    Meindert Niemeijer;Bram van Ginneken;Michael J Cree;Atsushi Mizutani

  • Classification of teeth in cone-beam CT using deep convolutional neural network

    Yuma Miki;Chisako Muramatsu;Tatsuro Hayashi;Xiangrong Zhou

  • Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

    Bram van Ginneken;Bram van Ginneken;Samuel G. Armato;Bartjan de Hoop;Saskia van Amelsvoort-van de Vorst

  • Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

    Motoki Fukuda;Kyoko Inamoto;Naoki Shibata;Yoshiko Ariji

  • Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks.

    Atsushi Teramoto;Tetsuya Tsukamoto;Yuka Kiriyama;Hiroshi Fujita

  • [AI-based computer-aided diagnosis (AI-CAD): The latest review to read first].

    Hiroshi Fujita

  • Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.

    Atsushi Teramoto;Hiroshi Fujita;Osamu Yamamuro;Tsuneo Tamaki

  • Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

    Makoto Murata;Yoshiko Ariji;Yasufumi Ohashi;Taisuke Kawai

  • Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

    Xiangrong Zhou;Ryosuke Takayama;Song Wang;Takeshi Hara

  • Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique.

    Yoshiko Ariji;Yudai Yanashita;Syota Kutsuna;Chisako Muramatsu

  • Investigation of basic imaging properties in digital radiography. 6. MTFs of II-TV digital imaging systems

    Hiroshi Fujita;Kunio Doi;Maryellen Lissak Giger

  • Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence.

    Yoshiko Ariji;Motoki Fukuda;Yoshitaka Kise;Michihito Nozawa

  • Automated microaneurysm detection method based on double ring filter in retinal fundus images

    Atsushi Mizutani;Chisako Muramatsu;Yuji Hatanaka;Shinsuke Suemori

  • Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs

    Hiroshi Fujita;Yoshikazu Uchiyama;Toshiaki Nakagawa;Daisuke Fukuoka

  • Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull's-eye images.

    Hiroshi Fujita;Tetsuro Katafuchi;Toshiisa Uehara;Tsunehiko Nishimura

  • Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter

    Atsushi Teramoto;Hiroshi Fujita

  • Basic imaging properties of a computed radiographic system with photostimulable phosphors.

    Hiroshi Fujita;Katsuhiko Ueda;Junji Morishita;Tsuyoshi Fujikawa

  • Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images.

    Xiangrong Zhou;Tatsuro Hayashi;Takeshi Hara;Hiroshi Fujita

Frequent Co-Authors

Kuniaki Saito
Kuniaki Saito Fujita Health University
Kunio Doi
Kunio Doi University of Chicago
Hirotaro Mori
Hirotaro Mori Osaka University
Song Wang
Song Wang University of South Carolina
Maryellen L. Giger
Maryellen L. Giger University of Chicago
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor

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