Janne J. Näppi
- Oncology top 2%
- Radiology, Nuclear Medicine and Imaging top 1%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Pulmonary and Respiratory Medicine top 10%
- Co-authors
- Hiroyuki YoshidaAbraham H. DachmanHans FrimmelPeter MacEneaneyKenji SuzukiOlli NevalainenWenli CaiDavid T. Rubin
- Topics
- Colorectal Cancer Screening and Detection (60 papers)Radiomics and Machine Learning in Medical Imaging (43 papers)AI in cancer detection (28 papers)
- Partner nations
- United StatesJapanSouth Korea
In The Last Decade
Janne J. Näppi
85 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 74
- Oncology 989
- Radiology, Nuclear Medicine and Imaging 904
- Artificial Intelligence 615
- Computer Vision and Pattern Recognition 469
- Pulmonary and Respiratory Medicine 374
Countries citing papers authored by Janne J. Näppi
This map shows the geographic impact of Janne J. Näppi'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 Janne J. Näppi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Janne J. Näppi more than expected).
Fields of papers citing papers by Janne J. Näppi
This network shows the impact of papers produced by Janne J. Näppi. 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 Janne J. Näppi. The network helps show where Janne J. Näppi may publish in the future.
Co-authorship network of co-authors of Janne J. Näppi
This figure shows the co-authorship network connecting the top 25 collaborators of Janne J. Näppi. A scholar is included among the top collaborators of Janne J. Näppi 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 Janne J. Näppi. Janne J. Näppi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 1 | |
| 3 | 13 | |
| 4 | 44 | |
| 5 | 1 | |
| 6 | 8 | |
| 7 | 20 | |
| 8 | 8 | |
| 9 | 12 | |
| 10 | 35 | |
| 11 | 43 | |
| 12 | 65 | |
| 13 | 52 | |
| 14 | 67 | |
| 15 | 29 | |
| 16 | 18 | |
| 17 | 58 | |
| 18 | 74 | |
| 19 | 47 | |
| 20 | 65 |
About Janne J. Näppi
Janne J. Näppi is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Computer Vision and Pattern Recognition, having authored 89 papers that have together received 1.6k indexed citations. Recurring topics across this work include Colorectal Cancer Screening and Detection (60 papers), Radiomics and Machine Learning in Medical Imaging (43 papers) and AI in cancer detection (28 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (904 citations), Oncology (989 citations) and Computer Vision and Pattern Recognition (469 citations). Janne J. Näppi has collaborated with scholars based in United States, Japan and South Korea. Frequent co-authors include Hiroyuki Yoshida, Hiroyuki Yoshida, Abraham H. Dachman, Hans Frimmel, Peter MacEneaney, Kenji Suzuki, Olli Nevalainen, Wenli Cai, David T. Rubin and Michael E. Zalis. Their work appears in journals such as Annals of Internal Medicine, Scientific Reports and IEEE Transactions on Medical Imaging.
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.