Ruchika Verma
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 2%
- Biophysics top 2%
- Biomedical Engineering
- Co-authors
- Neeraj KumarAmit SethiAbhishek VahadanePallavi TiwariRamón CorreaVirginia HillAnant MadabhushiNiha Beig
- Topics
- Radiomics and Machine Learning in Medical Imaging (20 papers)AI in cancer detection (10 papers)Glioma Diagnosis and Treatment (9 papers)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Ruchika Verma
27 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 583
- Radiology, Nuclear Medicine and Imaging 499
- Computer Vision and Pattern Recognition 463
- Biophysics 161
- Biomedical Engineering 137
Countries citing papers authored by Ruchika Verma
This map shows the geographic impact of Ruchika Verma'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 Ruchika Verma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruchika Verma more than expected).
Fields of papers citing papers by Ruchika Verma
This network shows the impact of papers produced by Ruchika Verma. 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 Ruchika Verma. The network helps show where Ruchika Verma may publish in the future.
Co-authorship network of co-authors of Ruchika Verma
This figure shows the co-authorship network connecting the top 25 collaborators of Ruchika Verma. A scholar is included among the top collaborators of Ruchika Verma 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 Ruchika Verma. Ruchika Verma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 15 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 15 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 15 | |
| 8 | 5 | |
| 9 | 6 | |
| 10 | 0 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 49 | |
| 14 | 92 | |
| 15 | 28 | |
| 16 | 3 | |
| 17 | A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathologybreakdown → | 671 |
| 18 | 37 | |
| 19 | 7 | |
| 20 | 39 |
About Ruchika Verma
Ruchika Verma is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Artificial Intelligence, having authored 30 papers that have together received 1.0k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (20 papers), AI in cancer detection (10 papers) and Glioma Diagnosis and Treatment (9 papers). The work is most often cited by research in Biophysics (161 citations), Computer Vision and Pattern Recognition (463 citations) and Radiology, Nuclear Medicine and Imaging (499 citations). Ruchika Verma has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Neeraj Kumar, Amit Sethi, Abhishek Vahadane, Pallavi Tiwari, Ramón Correa, Virginia Hill, Anant Madabhushi, Niha Beig, Manmeet S. Ahluwalia and Kaustav Bera. Their work appears in journals such as Nature Communications, Cancer Research and Clinical Cancer Research.
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.