Renuka Uppaluri

1.2k total citations
15 papers, 918 citations indexed

About

Renuka Uppaluri is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Renuka Uppaluri has authored 15 papers receiving a total of 918 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Pulmonary and Respiratory Medicine, 9 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Artificial Intelligence. Recurrent topics in Renuka Uppaluri's work include Radiomics and Machine Learning in Medical Imaging (4 papers), Radiation Dose and Imaging (4 papers) and Digital Radiography and Breast Imaging (4 papers). Renuka Uppaluri is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), Radiation Dose and Imaging (4 papers) and Digital Radiography and Breast Imaging (4 papers). Renuka Uppaluri collaborates with scholars based in United States and Germany. Renuka Uppaluri's co-authors include Geoffrey McLennan, Eric A. Hoffman, Milan Sonka, Gary W. Hunninghake, Theophano Mitsa, Patrick G. Hartley, Gopal Avinash, Baojun Li, Jeffrey R. Galvin and Timothy W. Deller and has published in prestigious journals such as American Journal of Respiratory and Critical Care Medicine, Academic Radiology and Pediatric Radiology.

In The Last Decade

Renuka Uppaluri

15 papers receiving 884 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Renuka Uppaluri United States 9 673 270 137 118 105 15 918
Saher Burhan Shaker Denmark 22 1.1k 1.7× 315 1.2× 271 2.0× 56 0.5× 57 0.5× 62 1.5k
Hyun J. Kim United States 21 946 1.4× 320 1.2× 153 1.1× 77 0.7× 39 0.4× 32 1.1k
Sushravya Raghunath United States 12 605 0.9× 283 1.0× 131 1.0× 89 0.8× 60 0.6× 27 822
Farbod N. Rahaghi United States 16 663 1.0× 234 0.9× 77 0.6× 63 0.5× 45 0.4× 56 960
Samuel Y. Ash United States 18 735 1.1× 207 0.8× 200 1.5× 49 0.4× 50 0.5× 49 1.0k
Pechin Lo Denmark 17 515 0.8× 421 1.6× 57 0.4× 110 0.9× 29 0.3× 42 817
Lucio Calandriello Italy 14 916 1.4× 377 1.4× 318 2.3× 83 0.7× 40 0.4× 42 1.2k
J W Gurney United States 16 872 1.3× 382 1.4× 43 0.3× 60 0.5× 80 0.8× 26 1.1k
Jan‐Martin Kuhnigk Germany 18 880 1.3× 943 3.5× 40 0.3× 175 1.5× 69 0.7× 44 1.4k
Mathieu Salaün France 18 641 1.0× 110 0.4× 41 0.3× 127 1.1× 32 0.3× 66 1.0k

Countries citing papers authored by Renuka Uppaluri

Since Specialization
Citations

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

Fields of papers citing papers by Renuka Uppaluri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Renuka Uppaluri

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

All Works

15 of 15 papers shown
1.
Deller, Timothy W., et al.. (2007). Effect of acquisition parameters on image quality in digital tomosynthesis. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6510. 65101L–65101L. 25 indexed citations
2.
Li, Baojun, et al.. (2006). Measurement of slice thickness and in-plane resolution on radiographic tomosynthesis system using modulation transfer function (MTF). Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6142. 61425D–61425D. 12 indexed citations
3.
Uppaluri, Renuka, et al.. (2004). Management of pediatric radiation dose using GE?s Revolution digital radiography systems. Pediatric Radiology. 34(S3). S215–S220. 3 indexed citations
4.
Li, Baojun, Gopal Avinash, Renuka Uppaluri, Jeffrey W. Eberhard, & Bernhard E. H. Claus. (2004). The impact of acquisition angular range on the z-resolution of radiographic tomosynthesis. International Congress Series. 1268. 13–18. 14 indexed citations
5.
Madsen, Mark T., Renuka Uppaluri, Eric A. Hoffman, & Geoffrey McLennan. (2002). Pulmonary CT image classification using evolutionary programming. 1997 IEEE Nuclear Science Symposium Conference Record. 2. 1179–1182. 4 indexed citations
6.
Avinash, Gopal, et al.. (2002). Effective dose reduction in dual-energy flat panel x-ray imaging: technique and clinical evaluation. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4684. 1048–1048. 9 indexed citations
7.
Uppaluri, Renuka, Eric A. Hoffman, Milan Sonka, et al.. (1999). Computer Recognition of Regional Lung Disease Patterns. American Journal of Respiratory and Critical Care Medicine. 160(2). 648–654. 179 indexed citations
8.
Uppaluri, Renuka, Eric A. Hoffman, Milan Sonka, Gary W. Hunninghake, & Geoffrey McLennan. (1999). Interstitial Lung Disease: A Quantitative Study Using the Adaptive Multiple Feature Method. American Journal of Respiratory and Critical Care Medicine. 159(2). 519–525. 417 indexed citations
9.
Uppaluri, Renuka, et al.. (1999). A Quantitative Study Using the Adaptive Multiple Feature Method. 1 indexed citations
10.
Madsen, Mark T., Renuka Uppaluri, Eric A. Hoffman, & Geoffrey McLennan. (1999). Pulmonary CT image classification with evolutionary programming. Academic Radiology. 6(12). 736–741. 4 indexed citations
11.
Uppaluri, Renuka, Geoffrey McLennan, Milan Sonka, & Eric A. Hoffman. (1998). <title>Computer-based objective quantitative assessment of pulmonary parenchyma via x-ray CT</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3337. 377–383. 8 indexed citations
12.
Uppaluri, Renuka, et al.. (1998). <title>Adaptive multiple feature method (AMFM) for early detecton of parenchymal pathology in a smoking population</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3337. 8–13. 5 indexed citations
13.
Uppaluri, Renuka, Theophano Mitsa, Milan Sonka, Eric A. Hoffman, & Geoffrey McLennan. (1997). Quantification of Pulmonary Emphysema from Lung Computed Tomography Images. American Journal of Respiratory and Critical Care Medicine. 156(1). 248–254. 213 indexed citations
14.
Uppaluri, Renuka, Theophano Mitsa, Eric A. Hoffman, Geoffrey McLennan, & Milan Sonka. (1996). <title>Texture analysis of pulmonary parenchyma in normal and emphysematous lung</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2709. 456–467. 8 indexed citations
15.
Uppaluri, Renuka, Theophano Mitsa, & Jeffrey R. Galvin. (1995). Fractal analysis of high-resolution CT images as a tool for quantification of lung diseases. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 2433. 133–133. 16 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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