Philip Chikontwe
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Cognitive Neuroscience
- Biomedical Engineering
- Co-authors
- Sang Hyun ParkSoopil KimMiguel A. Cabra de LunaHyo Jong LeeJune Hong AhnKyung Soo HongWeili LinDinggang Shen
- Topics
- AI in cancer detection (6 papers)Domain Adaptation and Few-Shot Learning (6 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
- Partner nations
- South KoreaUnited StatesChina
In The Last Decade
Philip Chikontwe
25 papers receiving 319 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 152
- Computer Vision and Pattern Recognition 120
- Radiology, Nuclear Medicine and Imaging 84
- Cognitive Neuroscience 48
- Biomedical Engineering 31
Countries citing papers authored by Philip Chikontwe
This map shows the geographic impact of Philip Chikontwe'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 Philip Chikontwe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Chikontwe more than expected).
Fields of papers citing papers by Philip Chikontwe
This network shows the impact of papers produced by Philip Chikontwe. 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 Philip Chikontwe. The network helps show where Philip Chikontwe may publish in the future.
Co-authorship network of co-authors of Philip Chikontwe
This figure shows the co-authorship network connecting the top 25 collaborators of Philip Chikontwe. A scholar is included among the top collaborators of Philip Chikontwe 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 Philip Chikontwe. Philip Chikontwe 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 | 6 | |
| 3 | 5 | |
| 4 | 5 | |
| 5 | 5 | |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 14 | |
| 9 | 5 | |
| 10 | 5 | |
| 11 | 24 | |
| 12 | 1 | |
| 13 | 15 | |
| 14 | 30 | |
| 15 | 17 | |
| 16 | 33 | |
| 17 | 4 | |
| 18 | 24 | |
| 19 | 15 | |
| 20 | 16 |
About Philip Chikontwe
Philip Chikontwe is a scholar working on Computer Vision and Pattern Recognition, Health Informatics and Artificial Intelligence, having authored 26 papers that have together received 320 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Health Informatics (13 citations), Computer Vision and Pattern Recognition (120 citations) and Artificial Intelligence (152 citations). Philip Chikontwe has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Sang Hyun Park, Soopil Kim, Miguel A. Cabra de Luna, Hyo Jong Lee, June Hong Ahn, Kyung Soo Hong, Weili Lin, Dinggang Shen, Xiaopeng Zong and Heounjeong Go. Their work appears in journals such as Expert Systems with Applications, IEEE Access 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.