Ruchika Verma

2.6k total citations · 1 hit paper
30 papers, 1.0k citations indexed

About

Ruchika Verma is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Genetics. According to data from OpenAlex, Ruchika Verma has authored 30 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Artificial Intelligence and 9 papers in Genetics. Recurrent topics in Ruchika Verma's work include Radiomics and Machine Learning in Medical Imaging (20 papers), AI in cancer detection (10 papers) and Glioma Diagnosis and Treatment (9 papers). Ruchika Verma is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (20 papers), AI in cancer detection (10 papers) and Glioma Diagnosis and Treatment (9 papers). Ruchika Verma collaborates with scholars based in United States, India and Canada. Ruchika Verma's co-authors include Neeraj Kumar, Amit Sethi, Abhishek Vahadane, Pallavi Tiwari, Ramón Correa, Virginia Hill, Anant Madabhushi, Niha Beig, Kaustav Bera and Volodymyr Statsevych and has published in prestigious journals such as Nature Communications, Cancer Research and Clinical Cancer Research.

In The Last Decade

Ruchika Verma

27 papers receiving 1.0k citations

Hit Papers

A Dataset and a Technique... 2017 2026 2020 2023 2017 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
    2017 671 citations Neeraj Kumar, Ruchika Verma et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ruchika Verma United States 10 583 499 463 161 137 30 1.0k
Jyoti Kini India 16 495 0.8× 326 0.7× 279 0.6× 80 0.5× 75 0.5× 69 828
Fujun Liu United States 10 439 0.8× 246 0.5× 301 0.7× 149 0.9× 78 0.6× 21 870
Ángel Cruz-Roa Colombia 13 1.4k 2.4× 852 1.7× 776 1.7× 206 1.3× 110 0.8× 40 1.8k
Abhishek Vahadane India 8 1.0k 1.7× 590 1.2× 708 1.5× 276 1.7× 128 0.9× 10 1.3k
Chung‐Ming Lo Taiwan 21 564 1.0× 746 1.5× 284 0.6× 19 0.1× 157 1.1× 62 1.2k
Pekka Ruusuvuori Finland 22 378 0.6× 203 0.4× 266 0.6× 433 2.7× 98 0.7× 80 1.3k
Marcial García‐Rojo Spain 15 349 0.6× 181 0.4× 155 0.3× 153 1.0× 43 0.3× 57 682
N. K. Timofeeva Netherlands 4 717 1.2× 471 0.9× 292 0.6× 143 0.9× 79 0.6× 10 963
Mark D. Zarella United States 12 681 1.2× 444 0.9× 199 0.4× 234 1.5× 69 0.5× 30 1.1k
Philipp Kainz Austria 10 605 1.0× 402 0.8× 404 0.9× 150 0.9× 62 0.5× 17 1.1k

Countries citing papers authored by Ruchika Verma

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Campanella, Gabriele, Shengjia Chen, Ruchika Verma, et al.. (2025). A clinical benchmark of public self-supervised pathology foundation models. Nature Communications. 16(1). 3640–3640. 15 indexed citations
2.
Gann, Peter H., et al.. (2024). Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images. Cancer Research Communications. 5(1). 157–166. 2 indexed citations
3.
Verma, Ruchika, Tyler Alban, Mojgan Mokhtari, et al.. (2024). Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning. Science Advances. 10(34). eadi0302–eadi0302. 4 indexed citations
4.
Verma, Ruchika, et al.. (2024). Artificial intelligence and machine learning applications for cultured meat. Frontiers in Artificial Intelligence. 7. 1424012–1424012. 15 indexed citations
5.
Kumar, Neeraj, Daniel Skubleny, Michael Parkes, et al.. (2023). Learning Individual Survival Models from PanCancer Whole Transcriptome Data. Clinical Cancer Research. 29(19). 3924–3936. 1 indexed citations
6.
Qi, Shi-ang, Neeraj Kumar, Ruchika Verma, et al.. (2023). Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction. IEEE Transactions on Biomedical Engineering. 70(12). 3389–3400. 2 indexed citations
7.
Verma, Ruchika, Virginia Hill, Volodymyr Statsevych, et al.. (2022). Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study. American Journal of Neuroradiology. 43(8). 1115–1123. 15 indexed citations
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Kumar, Neeraj, Ruchika Verma, Cheng Lu, et al.. (2022). Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors. The Journal of Pathology. 257(1). 17–28. 6 indexed citations
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Verma, Ruchika, et al.. (2021). NEIM-01. PREDICTION OF RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA VIA RADIOMIC ANALYSIS ON CLINICAL MRI SCANS. Neuro-Oncology Advances. 3(Supplement_4). iv6–iv6. 1 indexed citations
12.
Pati, Sarthak, Ruchika Verma, Hamed Akbari, et al.. (2020). Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset. Medical Physics. 47(12). 6039–6052. 28 indexed citations
13.
Beig, Niha, Kaustav Bera, Prateek Prasanna, et al.. (2020). Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clinical Cancer Research. 26(8). 1866–1876. 92 indexed citations
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Kumar, Neeraj, et al.. (2017). A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology. IEEE Transactions on Medical Imaging. 36(7). 1550–1560. 671 indexed citations breakdown →
17.
Kumar, Neeraj, Ruchika Verma, & Amit Sethi. (2017). Convolutional neural networks for wavelet domain super resolution. Pattern Recognition Letters. 90. 65–71. 37 indexed citations
18.
Verma, Ruchika, Neeraj Kumar, Amit Sethi, & Peter H. Gann. (2016). Detecting multiple sub-types of breast cancer in a single patient. 2648–2652. 7 indexed citations
19.
Verma, Ruchika, et al.. (2012). Pathological and epidemiological factors associated with advanced stage at diagnosis of breast cancer. British Medical Bulletin. 103(1). 129–145. 39 indexed citations
20.
Verma, Ruchika & Ali Akoglu. (2008). A coarse grained and hybrid reconfigurable architecture with flexible NoC router for variable block size motion estimation. Proceedings - IEEE International Parallel and Distributed Processing Symposium. 1–8. 2 indexed citations

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.

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