Lewis D. Griffin

4.2k total citations
102 papers, 2.6k citations indexed

About

Lewis D. Griffin is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence. According to data from OpenAlex, Lewis D. Griffin has authored 102 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Computer Vision and Pattern Recognition, 16 papers in Biophysics and 15 papers in Artificial Intelligence. Recurrent topics in Lewis D. Griffin's work include Image Retrieval and Classification Techniques (22 papers), Medical Image Segmentation Techniques (20 papers) and Cell Image Analysis Techniques (12 papers). Lewis D. Griffin is often cited by papers focused on Image Retrieval and Classification Techniques (22 papers), Medical Image Segmentation Techniques (20 papers) and Cell Image Analysis Techniques (12 papers). Lewis D. Griffin collaborates with scholars based in United Kingdom, United States and Denmark. Lewis D. Griffin's co-authors include Andrew J. Newell, Nicolas Jaccard, Thomas W. Rogers, David J. Hawkes, Martin Lillholm, William R. Crum, David Hill, A. C. F. Colchester, Nicolas Szita and Jerone T. A. Andrews and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Journal of Biological Chemistry.

In The Last Decade

Lewis D. Griffin

99 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lewis D. Griffin United Kingdom 28 866 559 423 351 340 102 2.6k
İpek Oğuz United States 24 540 0.6× 886 1.6× 230 0.5× 402 1.1× 255 0.8× 118 2.6k
Arvid Lundervold Norway 29 1.0k 1.2× 1.2k 2.1× 260 0.6× 890 2.5× 247 0.7× 116 3.4k
Matthew McAuliffe United States 20 301 0.3× 537 1.0× 331 0.8× 178 0.5× 424 1.2× 51 2.0k
A. Santos Spain 27 768 0.9× 1.6k 2.8× 430 1.0× 247 0.7× 629 1.9× 192 3.6k
Grégoire Malandain France 36 2.3k 2.6× 1.6k 2.9× 564 1.3× 263 0.7× 844 2.5× 138 4.9k
Deborah Pareto Spain 28 287 0.3× 893 1.6× 413 1.0× 221 0.6× 323 0.9× 118 2.9k
Alain Trouvé France 28 1.9k 2.3× 1.2k 2.1× 220 0.5× 309 0.9× 336 1.0× 81 3.8k
Erhardt Barth Germany 28 1.6k 1.8× 346 0.6× 302 0.7× 746 2.1× 155 0.5× 128 3.3k
Tolga Taşdizen United States 32 1.6k 1.8× 510 0.9× 232 0.5× 143 0.4× 212 0.6× 158 3.8k
Viren Jain United States 14 732 0.8× 176 0.3× 454 1.1× 312 0.9× 108 0.3× 26 2.3k

Countries citing papers authored by Lewis D. Griffin

Since Specialization
Citations

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

Fields of papers citing papers by Lewis D. Griffin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lewis D. Griffin. 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 Lewis D. Griffin. The network helps show where Lewis D. Griffin may publish in the future.

Co-authorship network of co-authors of Lewis D. Griffin

This figure shows the co-authorship network connecting the top 25 collaborators of Lewis D. Griffin. A scholar is included among the top collaborators of Lewis D. Griffin 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 Lewis D. Griffin. Lewis D. Griffin 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.
Griffin, Lewis D., et al.. (2023). Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer. Cancers. 15(6). 1720–1720. 8 indexed citations
2.
Griffin, Lewis D., et al.. (2023). Large Language Models respond to Influence like Humans. 15–24. 4 indexed citations
3.
Mozes, Maximilian, Bennett Kleinberg, & Lewis D. Griffin. (2022). Identifying Human Strategies for Generating Word-Level Adversarial Examples. 6118–6126.
4.
Griffin, Lewis D., et al.. (2022). Machine Learning of Raman Spectroscopy Data for Classifying Cancers: a Review of the Recent Literature. Preprints.org. 10 indexed citations
5.
Griffin, Lewis D., et al.. (2022). Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature. Diagnostics. 12(6). 1491–1491. 37 indexed citations
6.
Mylonas, Dimitris & Lewis D. Griffin. (2020). Coherence of achromatic, primary and basic classes of colour categories. Vision Research. 175. 14–22. 7 indexed citations
7.
Bictash, Magda, et al.. (2020). Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit Finding. SLAS DISCOVERY. 26(2). 257–262. 36 indexed citations
8.
Griffin, Lewis D., et al.. (2018). A spatial frequency spectral peakedness model predicts discrimination performance of regularity in dot patterns. Vision Research. 149. 102–114. 3 indexed citations
9.
Griffin, Lewis D., et al.. (2018). “Unexpected Item in the Bagging Area”: Anomaly Detection in X-Ray Security Images. IEEE Transactions on Information Forensics and Security. 14(6). 1539–1553. 35 indexed citations
10.
Andrews, Jerone T. A., Nicolas Jaccard, Thomas W. Rogers, & Lewis D. Griffin. (2017). Representation-learning for anomaly detection in complex x-ray cargo imagery. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10187. 101870E–101870E. 15 indexed citations
11.
Johnston, Alan, et al.. (2016). Difference magnitude is not measured by discrimination steps for order of point patterns. Journal of Vision. 16(9). 2–2. 9 indexed citations
12.
Baum, Buzz, et al.. (2014). An absolute interval scale of order for point patterns. Journal of The Royal Society Interface. 11(99). 20140342–20140342. 7 indexed citations
13.
Griffin, Lewis D., et al.. (2013). Distributional Learning of Appearance. PLoS ONE. 8(2). e58074–e58074. 3 indexed citations
14.
Watson, Ben, Sarah J. Wilson, Lewis D. Griffin, et al.. (2011). A pilot study of the effectiveness of d-cycloserine during cue-exposure therapy in abstinent alcohol-dependent subjects. Psychopharmacology. 216(1). 121–129. 28 indexed citations
15.
Griffin, Lewis D., et al.. (2007). Gradient direction dependencies in natural images. Spatial Vision. 20(3). 277–299. 2 indexed citations
16.
Crum, William R., Lewis D. Griffin, David Hill, & David J. Hawkes. (2003). Zen and the art of medical image registration: correspondence, homology, and quality. NeuroImage. 20(3). 1425–1437. 152 indexed citations
17.
Lillholm, Martin, Mads Nielsen, & Lewis D. Griffin. (2003). Feature-Based Image Analysis. International Journal of Computer Vision. 52(2-3). 73–95. 26 indexed citations
18.
Axer, Hubertus, et al.. (2002). A 3D fiber model of the human brainstem. Computerized Medical Imaging and Graphics. 26(6). 439–444. 11 indexed citations
19.
Griffin, Lewis D.. (1994). The Intrinsic Geometry of the Cerebral Cortex. Journal of Theoretical Biology. 166(3). 261–273. 68 indexed citations
20.
Griffin, Lewis D., et al.. (1992). Scale and segmentation of grey-level images using maximum gradient paths. Image and Vision Computing. 10(6). 389–402. 39 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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