Akiko Kano
- Radiology, Nuclear Medicine and Imaging top 10%
- Pulmonary and Respiratory Medicine
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Biomedical Engineering
- Co-authors
- Maryellen L. GigerKunio DoiHeber MacMahonKatsumi AbeTsuneo MatsumotoHitoshi YoshimuraSteven M. MontnerToru Yanagisawa
- Topics
- Radiomics and Machine Learning in Medical Imaging (5 papers)Medical Imaging Techniques and Applications (3 papers)Lung Cancer Diagnosis and Treatment (3 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingPulmonary and Respiratory MedicineArtificial Intelligence
- Partner nations
- JapanUnited States
In The Last Decade
Akiko Kano
13 papers receiving 287 citations
Peers
Comparison fields: 5 of 62
- Radiology, Nuclear Medicine and Imaging 216
- Pulmonary and Respiratory Medicine 154
- Artificial Intelligence 95
- Computer Vision and Pattern Recognition 53
- Biomedical Engineering 52
Countries citing papers authored by Akiko Kano
This map shows the geographic impact of Akiko Kano'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 Akiko Kano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akiko Kano more than expected).
Fields of papers citing papers by Akiko Kano
This network shows the impact of papers produced by Akiko Kano. 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 Akiko Kano. The network helps show where Akiko Kano may publish in the future.
Co-authorship network of co-authors of Akiko Kano
This figure shows the co-authorship network connecting the top 25 collaborators of Akiko Kano. A scholar is included among the top collaborators of Akiko Kano 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 Akiko Kano. Akiko Kano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 10 | |
| 3 | 5 | |
| 4 | 2 | |
| 5 | 8 | |
| 6 | 3 | |
| 7 | Effect of temporal subtraction technique on the diagnosis of primary lung cancer with chest radiography. | 1 |
| 8 | 5 | |
| 9 | 157 | |
| 10 | 25 | |
| 11 | 69 | |
| 12 | 6 | |
| 13 | 8 |
About Akiko Kano
Akiko Kano is a scholar working on Radiology, Nuclear Medicine and Imaging, Sensory Systems and Pulmonary and Respiratory Medicine, having authored 13 papers that have together received 302 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), Medical Imaging Techniques and Applications (3 papers) and Lung Cancer Diagnosis and Treatment (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (216 citations), Pulmonary and Respiratory Medicine (154 citations) and Artificial Intelligence (95 citations). Akiko Kano has collaborated with scholars based in Japan and United States. Frequent co-authors include Maryellen L. Giger, Kunio Doi, Heber MacMahon, Katsumi Abe, Tsuneo Matsumoto, Hitoshi Yoshimura, Steven M. Montner, Toru Yanagisawa, Xuan Chen and Hiroki Onuma. Their work appears in journals such as Journal of the American Academy of Dermatology, Medical Physics and European Radiology.
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.