Vivek Natarajan
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 10%
- Oncology
- Pulmonary and Respiratory Medicine
- Co-authors
- Mohammad NorouziTing ChenShekoofeh AziziBasil MustafaAaron LohSimon KornblithJan FreybergAlan Karthikesalingam
- Topics
- AI in cancer detection (2 papers)Scoliosis diagnosis and treatment (2 papers)Machine Learning in Healthcare (1 paper)
- Journals
- Journal of Pediatric OrthopaedicsThe Lancet Digital HealthThe International Journal of Spine Surgery
- Partner nations
- United StatesGermany
In The Last Decade
Vivek Natarajan
7 papers receiving 356 citations
Hit Papers
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 204
- Radiology, Nuclear Medicine and Imaging 196
- Computer Vision and Pattern Recognition 89
- Oncology 35
- Pulmonary and Respiratory Medicine 34
Countries citing papers authored by Vivek Natarajan
This map shows the geographic impact of Vivek Natarajan'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 Vivek Natarajan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vivek Natarajan more than expected).
Fields of papers citing papers by Vivek Natarajan
This network shows the impact of papers produced by Vivek Natarajan. 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 Vivek Natarajan. The network helps show where Vivek Natarajan may publish in the future.
Co-authorship network of co-authors of Vivek Natarajan
This figure shows the co-authorship network connecting the top 25 collaborators of Vivek Natarajan. A scholar is included among the top collaborators of Vivek Natarajan 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 Vivek Natarajan. Vivek Natarajan 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 | Big Self-Supervised Models Advance Medical Image Classificationbreakdown → | 336 |
| 4 | 2 | |
| 5 | DermGAN: Synthetic Generation of Clinical Skin Images with Pathology | 12 |
| 6 | 4 | |
| 7 | 6 |
About Vivek Natarajan
Vivek Natarajan is a scholar working on Health Informatics, Critical Care and Intensive Care Medicine and Biophysics, having authored 7 papers that have together received 367 indexed citations. Recurring topics across this work include AI in cancer detection (2 papers), Scoliosis diagnosis and treatment (2 papers) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (34 citations), Radiology, Nuclear Medicine and Imaging (196 citations) and Artificial Intelligence (204 citations). Vivek Natarajan has collaborated with scholars based in United States and Germany. Frequent co-authors include Mohammad Norouzi, Ting Chen, Shekoofeh Azizi, Basil Mustafa, Aaron Loh, Simon Kornblith, Jan Freyberg, Alan Karthikesalingam, Fiona Ryan and Amirata Ghorbani. Their work appears in journals such as Journal of Pediatric Orthopaedics, The Lancet Digital Health and The International Journal of Spine Surgery.
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.