Niha Beig
- Radiology, Nuclear Medicine and Imaging top 0.5%
- Pulmonary and Respiratory Medicine top 5%
- Genetics top 2%
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Co-authors
- Prateek PrasannaAnant MadabhushiVamsidhar VelchetiKaustav BeraRajat ThawaniPallavi TiwariSoumya GhoseMichael J. McLane
- Topics
- Radiomics and Machine Learning in Medical Imaging (28 papers)Glioma Diagnosis and Treatment (14 papers)Lung Cancer Diagnosis and Treatment (9 papers)
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Niha Beig
31 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 86
- Radiology, Nuclear Medicine and Imaging 1.5k
- Pulmonary and Respiratory Medicine 672
- Genetics 354
- Biomedical Engineering 339
- Artificial Intelligence 282
Countries citing papers authored by Niha Beig
This map shows the geographic impact of Niha Beig'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 Niha Beig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niha Beig more than expected).
Fields of papers citing papers by Niha Beig
This network shows the impact of papers produced by Niha Beig. 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 Niha Beig. The network helps show where Niha Beig may publish in the future.
Co-authorship network of co-authors of Niha Beig
This figure shows the co-authorship network connecting the top 25 collaborators of Niha Beig. A scholar is included among the top collaborators of Niha Beig 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 Niha Beig. Niha Beig is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 32 | |
| 3 | 16 | |
| 4 | 140 | |
| 5 | 49 | |
| 6 | 92 | |
| 7 | 28 | |
| 8 | 51 | |
| 9 | 96 | |
| 10 | 36 | |
| 11 | Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy forHER2 (ERBB2)–Positive Breast Cancerbreakdown → | 235 |
| 12 | 19 | |
| 13 | 238 | |
| 14 | 74 | |
| 15 | 114 | |
| 16 | 21 | |
| 17 | Radiomics and radiogenomics in lung cancer: A review for the clinicianbreakdown → | 349 |
| 18 | 10 | |
| 19 | 1 | |
| 20 | 4 |
About Niha Beig
Niha Beig is a scholar working on Genetics, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine, having authored 31 papers that have together received 1.7k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (28 papers), Glioma Diagnosis and Treatment (14 papers) and Lung Cancer Diagnosis and Treatment (9 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.5k citations), Health Informatics (55 citations) and Genetics (354 citations). Niha Beig has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Prateek Prasanna, Anant Madabhushi, Vamsidhar Velcheti, Kaustav Bera, Rajat Thawani, Pallavi Tiwari, Soumya Ghose, Michael J. McLane, Virginia Hill and Nathaniel Braman. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology and Scientific Reports.
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.