Varun Gulshan

9.4k total citations · 3 hit papers
9 papers, 5.8k citations indexed

About

Varun Gulshan is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Ophthalmology. According to data from OpenAlex, Varun Gulshan has authored 9 papers receiving a total of 5.8k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Ophthalmology. Recurrent topics in Varun Gulshan's work include Retinal Imaging and Analysis (4 papers), Retinal Diseases and Treatments (3 papers) and Retinal and Optic Conditions (2 papers). Varun Gulshan is often cited by papers focused on Retinal Imaging and Analysis (4 papers), Retinal Diseases and Treatments (3 papers) and Retinal and Optic Conditions (2 papers). Varun Gulshan collaborates with scholars based in United States, India and United Kingdom. Varun Gulshan's co-authors include Dale R. Webster, Lily Peng, Kasumi Widner, Kim Ramasamy, Marc Coram, Rajiv Raman, Derek Wu, Martin C. Stumpe, T. Madams and Jessica L. Mega and has published in prestigious journals such as JAMA, SHILAP Revista de lepidopterología and Ophthalmology.

In The Last Decade

Varun Gulshan

9 papers receiving 5.6k citations

Hit Papers

Development and Validatio... 2009 2026 2014 2020 2016 2009 2018 1000 2.0k 3.0k 4.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Varun Gulshan United States 8 3.2k 1.8k 1.5k 1.4k 690 9 5.8k
Arunachalam Narayanaswamy United States 12 3.0k 0.9× 1.6k 0.9× 852 0.6× 1.2k 0.9× 678 1.0× 15 5.3k
Jorge Cuadros United States 14 3.5k 1.1× 2.0k 1.2× 836 0.6× 1.2k 0.8× 667 1.0× 32 5.3k
Kasumi Widner United States 4 3.2k 1.0× 1.8k 1.0× 806 0.5× 1.2k 0.9× 703 1.0× 7 5.1k
Derek Wu Canada 9 3.2k 1.0× 1.7k 0.9× 774 0.5× 1.2k 0.9× 706 1.0× 18 5.1k
Marc Coram United States 19 2.9k 0.9× 1.6k 0.9× 740 0.5× 1.2k 0.9× 648 0.9× 30 6.2k
T. Madams United States 3 2.8k 0.9× 1.4k 0.8× 716 0.5× 1.1k 0.8× 627 0.9× 6 4.5k
Kim Ramasamy India 28 4.3k 1.3× 3.5k 2.0× 760 0.5× 1.2k 0.8× 658 1.0× 141 7.1k
Yun Liu United States 29 2.4k 0.7× 719 0.4× 847 0.6× 2.0k 1.4× 877 1.3× 112 5.4k
Rajiv Raman India 34 5.6k 1.7× 4.5k 2.6× 896 0.6× 1.2k 0.9× 701 1.0× 232 8.8k
Subhashini Venugopalan United States 13 3.0k 0.9× 1.5k 0.9× 5.2k 3.5× 3.3k 2.4× 662 1.0× 23 10.5k

Countries citing papers authored by Varun Gulshan

Since Specialization
Citations

This map shows the geographic impact of Varun Gulshan'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 Varun Gulshan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Varun Gulshan more than expected).

Fields of papers citing papers by Varun Gulshan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Varun Gulshan. 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 Varun Gulshan. The network helps show where Varun Gulshan may publish in the future.

Co-authorship network of co-authors of Varun Gulshan

This figure shows the co-authorship network connecting the top 25 collaborators of Varun Gulshan. A scholar is included among the top collaborators of Varun Gulshan 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 Varun Gulshan. Varun Gulshan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Nearing, Grey, et al.. (2023). A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval. Journal of Hydrometeorology. 24(10). 1789–1823. 15 indexed citations
2.
Dror, Gideon, et al.. (2023). Cross-modal distillation for flood extent mapping. SHILAP Revista de lepidopterología. 2. 11 indexed citations
3.
Sayres, Rory, et al.. (2020). Challenges in evaluating clinical deployments of Deep Learning Assisted Diagnostics for Diabetic Retinopathy Screening. Investigative Ophthalmology & Visual Science. 61(7). 2045–2045. 2 indexed citations
4.
Gulshan, Varun, Renu P. Rajan, Kasumi Widner, et al.. (2019). Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmology. 137(9). 987–987. 181 indexed citations
5.
Krause, Jonathan, Varun Gulshan, Ehsan Rahimy, et al.. (2018). Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. Ophthalmology. 125(8). 1264–1272. 358 indexed citations breakdown →
6.
Gulshan, Varun, Lily Peng, Marc Coram, et al.. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 316(22). 2402–2402. 4490 indexed citations breakdown →
7.
Gulshan, Varun, Victor Lempitsky, & Andrew Zisserman. (2011). Humanising GrabCut: Learning to segment humans using the Kinect. 1127–1133. 27 indexed citations
8.
Gulshan, Varun, Carsten Rother, Antonio Criminisi, Andrew Blake, & Andrew Zisserman. (2010). Geodesic star convexity for interactive image segmentation. 3129–3136. 260 indexed citations
9.
Vedaldi, Andrea, Varun Gulshan, Manik Varma, & Andrew Zisserman. (2009). Multiple kernels for object detection. 606–613. 499 indexed citations breakdown →

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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