Masayuki Tsuneki
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Oncology top 10%
- Molecular Biology
- Pulmonary and Respiratory Medicine top 10%
- Co-authors
- Fahdi KanavatiJoseph A. MadriOsamu IizukaKoji ArihiroKei KatoSatoshi MaruyamaTakashi SakuJun Cheng
- Topics
- AI in cancer detection (17 papers)Colorectal Cancer Screening and Detection (9 papers)Radiomics and Machine Learning in Medical Imaging (9 papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Masayuki Tsuneki
50 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Artificial Intelligence 524
- Radiology, Nuclear Medicine and Imaging 439
- Oncology 386
- Molecular Biology 276
- Pulmonary and Respiratory Medicine 243
Countries citing papers authored by Masayuki Tsuneki
This map shows the geographic impact of Masayuki Tsuneki'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 Masayuki Tsuneki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masayuki Tsuneki more than expected).
Fields of papers citing papers by Masayuki Tsuneki
This network shows the impact of papers produced by Masayuki Tsuneki. 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 Masayuki Tsuneki. The network helps show where Masayuki Tsuneki may publish in the future.
Co-authorship network of co-authors of Masayuki Tsuneki
This figure shows the co-authorship network connecting the top 25 collaborators of Masayuki Tsuneki. A scholar is included among the top collaborators of Masayuki Tsuneki 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 Masayuki Tsuneki. Masayuki Tsuneki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 11 | |
| 5 | 7 | |
| 6 | 45 | |
| 7 | 7 | |
| 8 | 117 | |
| 9 | 39 | |
| 10 | Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumoursbreakdown → | 271 |
| 11 | 5 | |
| 12 | 29 | |
| 13 | 22 | |
| 14 | 9 | |
| 15 | 19 | |
| 16 | 33 | |
| 17 | 26 | |
| 18 | 31 | |
| 19 | 40 | |
| 20 | 6 |
About Masayuki Tsuneki
Masayuki Tsuneki is a scholar working on Oral Surgery, Oncology and Cell Biology, having authored 51 papers that have together received 1.5k indexed citations. Recurring topics across this work include AI in cancer detection (17 papers), Colorectal Cancer Screening and Detection (9 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). The work is most often cited by research in Health Informatics (42 citations), Radiology, Nuclear Medicine and Imaging (439 citations) and Oral Surgery (131 citations). Masayuki Tsuneki has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Fahdi Kanavati, Joseph A. Madri, Osamu Iizuka, Koji Arihiro, Kei Kato, Satoshi Maruyama, Takashi Saku, Jun Cheng, Manabu Yamazaki and Koji Yamazaki. Their work appears in journals such as Journal of Biological Chemistry, PLoS ONE and Molecular and Cellular Biology.
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.