Raghav Mehta

624 total citations
13 papers, 245 citations indexed

About

Raghav Mehta is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Raghav Mehta has authored 13 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Information Systems. Recurrent topics in Raghav Mehta's work include Medical Image Segmentation Techniques (3 papers), Advanced Neural Network Applications (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Raghav Mehta is often cited by papers focused on Medical Image Segmentation Techniques (3 papers), Advanced Neural Network Applications (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Raghav Mehta collaborates with scholars based in India, Canada and United Kingdom. Raghav Mehta's co-authors include Jayanthi Sivaswamy, Chun‐Nan Hsu, Yannis Katsis, Neetu Sardana, Chandrasekharan Kesavadas, Sheelakumari Raghavan, Tal Arbel, Yarin Gal, Douglas L. Arnold and T. R. Gopalakrishnan Nair and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Chromosoma and Journal of Pharmacy And Bioallied Sciences.

In The Last Decade

Raghav Mehta

11 papers receiving 237 citations

Peers

Raghav Mehta

CVPR

RNMI

AI

NEURO

BE

Zhuoyuan Li China

Mahsa Shakeri Canada

Yeşim Eroğlu Türkiye

Ivan Coronado United States

Tongtong Che China

Rui Hui China

Liyu Wei China

Farheen Ramzan Pakistan

Nirmala Madian India

Zhuoyuan Li China

Raghav Mehta

89 ×0.9

CVPR

121 ×1.3

RNMI

101 ×1.5

AI

54 ×1.0

NEURO

34 ×1.1

BE

Citations per year, relative to Raghav Mehta Raghav Mehta (= 1×) peers Zhuoyuan Li

Countries citing papers authored by Raghav Mehta

Since Specialization

Citations

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

Fields of papers citing papers by Raghav Mehta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Raghav Mehta

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

All Works

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13 of 13 papers shown

1.

Mehta, Raghav, et al.. (2024). Genetics and Malignant Hyperthermia. ASA Monitor. 89(2). 17–18.

2.

Mehta, Raghav, et al.. (2023). Evaluation of Peri-Implant Parameters and C-Reactive Protein Levels among Patients with Different Obesity Levels. Journal of Pharmacy And Bioallied Sciences. 15(Suppl 2). S1043–S1045.

3.

Nichyporuk, Brennan, et al.. (2022). Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation. 1(December 2022). 1–37. 4 indexed citations

4.

Mehta, Raghav, T. R. Gopalakrishnan Nair, Manuela Costantino, et al.. (2021). Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. IEEE Transactions on Medical Imaging. 41(2). 360–373. 16 indexed citations

5.

Sardana, Neetu, et al.. (2020). Analyzing Performance of Deep Learning Techniques for Web Navigation Prediction. Procedia Computer Science. 167. 1739–1748. 10 indexed citations

6.

Sivaswamy, Jayanthi, et al.. (2019). Construction of Indian human brain atlas. Neurology India. 67(1). 229–229. 15 indexed citations

7.

Katsis, Yannis, et al.. (2019). NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction. VBN Forskningsportal (Aalborg Universitet). 22 indexed citations

8.

Sardana, Neetu, et al.. (2019). Efficient web navigation prediction using hybrid models based on multiple evidence combinations. International Journal of Computers and Applications. 42(7). 715–728. 2 indexed citations

9.

Mehta, Raghav, et al.. (2018). High performance training of deep neural networks using pipelined hardware acceleration and distributed memory. 7. 383–388. 2 indexed citations

10.

Mehta, Raghav & Jayanthi Sivaswamy. (2017). M-net: A Convolutional Neural Network for deep brain structure segmentation. 437–440. 113 indexed citations

11.

Mehta, Raghav, et al.. (2017). BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures. Journal of Medical Imaging. 4(2). 24003–24003. 53 indexed citations

12.

Mehta, Raghav, et al.. (2014). RFID and ASP. NET based Campus Management System. International Journal of Computer Applications. 88(4). 29–32. 1 indexed citations

13.

Boyes, J. W., et al.. (1968). Chromosomes of Syrphidae. Chromosoma. 24(2). 233–242. 7 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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