Sema Candemir
- Radiology, Nuclear Medicine and Imaging top 0.5%
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 1%
- Pulmonary and Respiratory Medicine top 5%
- Media Technology top 1%
- Co-authors
- Sameer AntaniGeorge R. ThomaStefan JaegerYì WángZhiyun XueAlexandros KarargyrisPu-Xuan LuKannappan Palaniappan
- Topics
- COVID-19 diagnosis using AI (17 papers)AI in cancer detection (13 papers)Radiomics and Machine Learning in Medical Imaging (11 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Partner nations
- United StatesChinaGermany
In The Last Decade
Sema Candemir
39 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Radiology, Nuclear Medicine and Imaging 2.1k
- Artificial Intelligence 940
- Computer Vision and Pattern Recognition 691
- Pulmonary and Respiratory Medicine 611
- Media Technology 280
Countries citing papers authored by Sema Candemir
This map shows the geographic impact of Sema Candemir'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 Sema Candemir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sema Candemir more than expected).
Fields of papers citing papers by Sema Candemir
This network shows the impact of papers produced by Sema Candemir. 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 Sema Candemir. The network helps show where Sema Candemir may publish in the future.
Co-authorship network of co-authors of Sema Candemir
This figure shows the co-authorship network connecting the top 25 collaborators of Sema Candemir. A scholar is included among the top collaborators of Sema Candemir 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 Sema Candemir. Sema Candemir is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 6 | |
| 3 | 24 | |
| 4 | 109 | |
| 5 | 17 | |
| 6 | 35 | |
| 7 | 115 | |
| 8 | 42 | |
| 9 | 49 | |
| 10 | 23 | |
| 11 | 0 | |
| 12 | 21 | |
| 13 | 21 | |
| 14 | 18 | |
| 15 | 78 | |
| 16 | 15 | |
| 17 | Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.breakdown → | 611 |
| 18 | Persistent target tracking using likelihood fusion in wide-area and full motion video sequences | 51 |
| 19 | 4 | |
| 20 | 1 |
About Sema Candemir
Sema Candemir is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 40 papers that have together received 2.7k indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (17 papers), AI in cancer detection (13 papers) and Radiomics and Machine Learning in Medical Imaging (11 papers). The work is most often cited by research in Health Informatics (165 citations), Radiology, Nuclear Medicine and Imaging (2.1k citations) and Computer Vision and Pattern Recognition (691 citations). Sema Candemir has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Sameer Antani, George R. Thoma, Stefan Jaeger, Yì Wáng, Zhiyun Xue, Alexandros Karargyris, Pu-Xuan Lu, Kannappan Palaniappan, Rahul Kumar Singh and Les Folio. Their work appears in journals such as IEEE Access, IEEE Transactions on Medical Imaging and Applied Sciences.
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.