Yulia Arzhaeva
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Pulmonary and Respiratory Medicine
- Biomedical Engineering
- Co-authors
- Bram van GinnekenEva M. van RikxoortMax A. ViergeverMarius StaringJosien P. W. PluimIvana IšgumStefan KleinDavid M. J. Tax
- Topics
- AI in cancer detection (8 papers)COVID-19 diagnosis using AI (6 papers)Radiomics and Machine Learning in Medical Imaging (5 papers)
- Cited by
- Computer Vision and Pattern RecognitionRadiology, Nuclear Medicine and ImagingArtificial Intelligence
- Partner nations
- AustraliaNetherlandsGermany
In The Last Decade
Yulia Arzhaeva
24 papers receiving 406 citations
Peers
Comparison fields: 5 of 81
- Radiology, Nuclear Medicine and Imaging 174
- Computer Vision and Pattern Recognition 167
- Artificial Intelligence 113
- Pulmonary and Respiratory Medicine 95
- Biomedical Engineering 62
Countries citing papers authored by Yulia Arzhaeva
This map shows the geographic impact of Yulia Arzhaeva'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 Yulia Arzhaeva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yulia Arzhaeva more than expected).
Fields of papers citing papers by Yulia Arzhaeva
This network shows the impact of papers produced by Yulia Arzhaeva. 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 Yulia Arzhaeva. The network helps show where Yulia Arzhaeva may publish in the future.
Co-authorship network of co-authors of Yulia Arzhaeva
This figure shows the co-authorship network connecting the top 25 collaborators of Yulia Arzhaeva. A scholar is included among the top collaborators of Yulia Arzhaeva 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 Yulia Arzhaeva. Yulia Arzhaeva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 17 | |
| 5 | 12 | |
| 6 | 2 | |
| 7 | 9 | |
| 8 | 33 | |
| 9 | 18 | |
| 10 | 6 | |
| 11 | 5 | |
| 12 | 43 | |
| 13 | 17 | |
| 14 | 15 | |
| 15 | 120 | |
| 16 | 22 | |
| 17 | 28 | |
| 18 | Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching | 42 |
| 19 | 3 | |
| 20 | 3 |
About Yulia Arzhaeva
Yulia Arzhaeva is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Biophysics, having authored 25 papers that have together received 417 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), COVID-19 diagnosis using AI (6 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (167 citations), Radiology, Nuclear Medicine and Imaging (174 citations) and Artificial Intelligence (113 citations). Yulia Arzhaeva has collaborated with scholars based in Australia, Netherlands and Germany. Frequent co-authors include Bram van Ginneken, Eva M. van Rikxoort, Max A. Viergever, Marius Staring, Josien P. W. Pluim, Ivana Išgum, Stefan Klein, David M. J. Tax, Ryan Lagerstrom and Pim A. de Jong. Their work appears in journals such as Scientific Reports, Food Chemistry and Pattern Recognition.
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.