Kaman Chung
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Biomedical Engineering
- Co-authors
- Bram van GinnekenErnst T. ScholtenMathias ProkopPim A. de JongFrancesco CiompiMatthijs OudkerkSarah J. van RielBartjan de Hoop
- Topics
- Lung Cancer Diagnosis and Treatment (9 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)Lung Cancer Treatments and Mutations (5 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingHealth InformaticsPulmonary and Respiratory Medicine
- Journals
- PLoS ONEScientific ReportsRadiology
- Partner nations
- NetherlandsUnited StatesItaly
In The Last Decade
Kaman Chung
11 papers receiving 464 citations
Peers
Comparison fields: 5 of 63
- Radiology, Nuclear Medicine and Imaging 345
- Pulmonary and Respiratory Medicine 288
- Artificial Intelligence 132
- Computer Vision and Pattern Recognition 52
- Biomedical Engineering 46
Countries citing papers authored by Kaman Chung
This map shows the geographic impact of Kaman Chung'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 Kaman Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaman Chung more than expected).
Fields of papers citing papers by Kaman Chung
This network shows the impact of papers produced by Kaman Chung. 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 Kaman Chung. The network helps show where Kaman Chung may publish in the future.
Co-authorship network of co-authors of Kaman Chung
This figure shows the co-authorship network connecting the top 25 collaborators of Kaman Chung. A scholar is included among the top collaborators of Kaman Chung 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 Kaman Chung. Kaman Chung 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 | 29 | |
| 3 | 98 | |
| 4 | 3 | |
| 5 | 9 | |
| 6 | 33 | |
| 7 | 15 | |
| 8 | 13 | |
| 9 | 18 | |
| 10 | 43 | |
| 11 | 211 | |
| 12 | 6 |
About Kaman Chung
Kaman Chung is a scholar working on Pulmonary and Respiratory Medicine, Internal Medicine and Radiology, Nuclear Medicine and Imaging, having authored 12 papers that have together received 478 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Lung Cancer Treatments and Mutations (5 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (345 citations), Health Informatics (12 citations) and Pulmonary and Respiratory Medicine (288 citations). Kaman Chung has collaborated with scholars based in Netherlands, United States and Italy. Frequent co-authors include Bram van Ginneken, Ernst T. Scholten, Mathias Prokop, Pim A. de Jong, Francesco Ciompi, Matthijs Oudkerk, Sarah J. van Riel, Bartjan de Hoop, Colin Jacobs and Arnaud A. A. Setio. Their work appears in journals such as PLoS ONE, Scientific Reports and 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.