Kunio Doi

1.3k total citations
16 papers, 939 citations indexed

About

Kunio Doi is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Kunio Doi has authored 16 papers receiving a total of 939 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Pulmonary and Respiratory Medicine and 6 papers in Artificial Intelligence. Recurrent topics in Kunio Doi's work include AI in cancer detection (6 papers), COVID-19 diagnosis using AI (6 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Kunio Doi is often cited by papers focused on AI in cancer detection (6 papers), COVID-19 diagnosis using AI (6 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Kunio Doi collaborates with scholars based in United States and Germany. Kunio Doi's co-authors include Heber MacMahon, Robert A. Schmidt, Maryellen L. Giger, F Yin, Carl J. Vyborny, Charles E. Metz, Heang‐Ping Chan, Shigehiko Katsuragawa, Kwok L. Lam and Shigeru Sanada and has published in prestigious journals such as Radiology, Medical Physics and Investigative Radiology.

In The Last Decade

Kunio Doi

16 papers receiving 885 citations

Peers

Kunio Doi
Comparison fields: 5 of 76
  • Artificial Intelligence 610
  • Radiology, Nuclear Medicine and Imaging 568
  • Pulmonary and Respiratory Medicine 302
  • Computer Vision and Pattern Recognition 272
  • Oncology 128
Replace Shih‐Chung B. Lo with:
Shih‐Chung B. Lo United States
Miguel Souto Spain
Jun Ge United States
N. K. Timofeeva Netherlands
J Vidal Spain
Iringo Kovacs Netherlands
Jeffrey W. Hoffmeister United States
Wouter Bulten Netherlands
B. Wein Germany
Maxine Tan United States
Shih‐Chung B. Lo United States View profile →
Citations per field, relative to Kunio Doi
Kunio Doi · 1×
Citations per year, relative to Kunio Doi
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Countries citing papers authored by Kunio Doi

Since Specialization
Citations

This map shows the geographic impact of Kunio Doi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kunio Doi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kunio Doi more than expected).

Fields of papers citing papers by Kunio Doi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kunio Doi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kunio Doi. The network helps show where Kunio Doi may publish in the future.

Co-authorship network of co-authors of Kunio Doi

This figure shows the co-authorship network connecting the top 25 collaborators of Kunio Doi. A scholar is included among the top collaborators of Kunio Doi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kunio Doi. Kunio Doi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
# Work Indexed citations
1 31
2 17
3 98
4 6
5 111
6 67
7 49
8
Computer-aided diagnosis: development of automated schemes for quantitative analysis of radiographic images.
34
9 25
10 19
11 13
12 274
13 103
14 4
15 70
16 18

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026