Ryutaro Tanno
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Health Informatics top 2%
- Pediatrics, Perinatology and Child Health
- Co-authors
- Daniel C. AlexanderArdavan SaeediNathan SilbermanSwami SankaranarayananAurobrata GhoshEnrico KadenAntonio CriminisiStamatios N. Sotiropoulos
- Topics
- Machine Learning and Data Classification (5 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)Artificial Intelligence in Healthcare and Education (3 papers)
- Partner nations
- United KingdomUnited StatesNetherlands
In The Last Decade
Ryutaro Tanno
15 papers receiving 542 citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Radiology, Nuclear Medicine and Imaging 250
- Artificial Intelligence 225
- Computer Vision and Pattern Recognition 131
- Health Informatics 50
- Pediatrics, Perinatology and Child Health 36
Countries citing papers authored by Ryutaro Tanno
This map shows the geographic impact of Ryutaro Tanno'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 Ryutaro Tanno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryutaro Tanno more than expected).
Fields of papers citing papers by Ryutaro Tanno
This network shows the impact of papers produced by Ryutaro Tanno. 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 Ryutaro Tanno. The network helps show where Ryutaro Tanno may publish in the future.
Co-authorship network of co-authors of Ryutaro Tanno
This figure shows the co-authorship network connecting the top 25 collaborators of Ryutaro Tanno. A scholar is included among the top collaborators of Ryutaro Tanno 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 Ryutaro Tanno. Ryutaro Tanno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Generative models improve fairness of medical classifiers under distribution shiftsbreakdown → | 55 |
| 2 | 16 | |
| 3 | 4 | |
| 4 | 21 | |
| 5 | 55 | |
| 6 | 5 | |
| 7 | 20 | |
| 8 | Disentangling Human Error from Ground Truth in Segmentation of Medical Images | 1 |
| 9 | 65 | |
| 10 | 36 | |
| 11 | 65 | |
| 12 | 129 | |
| 13 | 8 | |
| 14 | Quality control in radiotherapy-treatment planning using multi-task learning and uncertainty estimation | 4 |
| 15 | 66 |
About Ryutaro Tanno
Ryutaro Tanno is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Applied Psychology, having authored 15 papers that have together received 550 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Artificial Intelligence in Healthcare and Education (3 papers). The work is most often cited by research in Health Informatics (50 citations), Radiology, Nuclear Medicine and Imaging (250 citations) and Artificial Intelligence (225 citations). Ryutaro Tanno has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Daniel C. Alexander, Ardavan Saeedi, Nathan Silberman, Swami Sankaranarayanan, Aurobrata Ghosh, Enrico Kaden, Antonio Criminisi, Stamatios N. Sotiropoulos, Francesco Grussu and Daniel E. Worrall. Their work appears in journals such as Nature Medicine, Nature Communications and PLoS ONE.
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.