S.J. McKenna

6.9k total citations
175 papers, 4.3k citations indexed

About

S.J. McKenna is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Surgery. According to data from OpenAlex, S.J. McKenna has authored 175 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Computer Vision and Pattern Recognition, 30 papers in Artificial Intelligence and 19 papers in Surgery. Recurrent topics in S.J. McKenna's work include Video Surveillance and Tracking Methods (25 papers), AI in cancer detection (17 papers) and Image Retrieval and Classification Techniques (16 papers). S.J. McKenna is often cited by papers focused on Video Surveillance and Tracking Methods (25 papers), AI in cancer detection (17 papers) and Image Retrieval and Classification Techniques (16 papers). S.J. McKenna collaborates with scholars based in United Kingdom, United States and China. S.J. McKenna's co-authors include Shaogang Gong, Yogesh Raja, Sebastian Stein, Hammadi Nait‐Charif, Harry Wechsler, Azriel Rosenfeld, Zoran Đurić, Timothy J. Roberts, Tracy A. Valentine and A. Glyn Bengough and has published in prestigious journals such as Cancer Research, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

S.J. McKenna

167 papers receiving 4.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S.J. McKenna United Kingdom 28 2.0k 674 651 371 307 175 4.3k
Xiaojun Chen China 36 786 0.4× 839 1.2× 180 0.3× 1.3k 3.5× 228 0.7× 279 4.3k
Sim Heng Ong Singapore 32 1.9k 0.9× 285 0.4× 555 0.9× 394 1.1× 117 0.4× 174 3.8k
Kelvin Weng Chiong Foong Singapore 21 511 0.2× 455 0.7× 140 0.2× 189 0.5× 37 0.1× 85 1.8k
James Graham United States 15 3.8k 1.8× 197 0.3× 630 1.0× 827 2.2× 39 0.1× 59 5.6k
Concetto Spampinato Italy 34 1.4k 0.7× 446 0.7× 636 1.0× 322 0.9× 69 0.2× 183 3.9k
Kazuyuki Kobayashi Japan 28 200 0.1× 202 0.3× 62 0.1× 386 1.0× 89 0.3× 289 3.0k
Renato Natal Jorge Portugal 50 372 0.2× 161 0.2× 137 0.2× 1.3k 3.4× 30 0.1× 372 9.0k
J. Paul Siebert United Kingdom 23 640 0.3× 238 0.4× 78 0.1× 200 0.5× 36 0.1× 84 1.8k
Hiroshi Fujita Japan 46 1.8k 0.9× 1.5k 2.3× 2.1k 3.3× 2.0k 5.3× 30 0.1× 423 9.2k
Mohamed Abdel-Mottaleb United States 33 3.0k 1.5× 564 0.8× 597 0.9× 416 1.1× 29 0.1× 132 4.5k

Countries citing papers authored by S.J. McKenna

Since Specialization
Citations

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

Fields of papers citing papers by S.J. McKenna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S.J. McKenna. 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 S.J. McKenna. The network helps show where S.J. McKenna may publish in the future.

Co-authorship network of co-authors of S.J. McKenna

This figure shows the co-authorship network connecting the top 25 collaborators of S.J. McKenna. A scholar is included among the top collaborators of S.J. McKenna 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 S.J. McKenna. S.J. McKenna 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.
McKenna, S.J., et al.. (2024). Unsupervised mapping and semantic user localisation from first-person monocular video. Pattern Recognition. 158. 110923–110923.
2.
Tian, Xu, Hanhe Lin, S.J. McKenna, et al.. (2023). Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(1). 57–68. 11 indexed citations
3.
McKenna, S.J., et al.. (2023). Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept. Biology. 12(4). 619–619. 3 indexed citations
4.
5.
Pritchard, M., Lydia C. Powell, Georgina Menzies, et al.. (2023). Structure–Activity Relationships of Low Molecular Weight Alginate Oligosaccharide Therapy against Pseudomonas aeruginosa. Biomolecules. 13(9). 1366–1366. 6 indexed citations
6.
McKenna, S.J., et al.. (2019). Predicting full-scale and verbal intelligence scores from functional Connectomic data in individuals with autism Spectrum disorder. Brain Imaging and Behavior. 14(5). 1769–1778. 20 indexed citations
7.
Fetit, Ahmed E., Alex S. F. Doney, Stephen Hogg, et al.. (2019). A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features. Scientific Reports. 9(1). 3591–3591. 23 indexed citations
8.
Tolomeo, Serenella, et al.. (2019). High-Throughput, Time-Resolved Mechanical Phenotyping of Prostate Cancer Cells. Scientific Reports. 9(1). 5742–5742. 11 indexed citations
9.
McKenna, S.J., et al.. (2018). Multi-part segmentation for porcine offal inspection with auto-context and adaptive atlases. Pattern Recognition Letters. 112. 290–296. 4 indexed citations
10.
Akbar, Shazia, Lee B. Jordan, Colin A. Purdie, Alastair M. Thompson, & S.J. McKenna. (2015). Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays. British Journal of Cancer. 113(7). 1075–1080. 27 indexed citations
11.
Li, Wenqi, et al.. (2013). Learning from Partially Annotated OPT Images by Contextual Relevance Ranking. Lecture notes in computer science. 16(Pt 3). 429–436. 1 indexed citations
12.
McKenna, S.J., et al.. (2013). Immunohistochemical analysis of breast tissue microarray images using contextual classifiers. Journal of Pathology Informatics. 4(2). 13–13. 16 indexed citations
13.
John, Vijay, Emanuele Trucco, & S.J. McKenna. (2010). Markerless human motion capture using charting and manifold constrained particle swarm optimisation. Discovery Research Portal (University of Dundee). 6 indexed citations
14.
McKenna, S.J., et al.. (2008). Automated assessment of polyethylene wear in cemented acetabular components using anteroposterior radiographs of total hip replacements. Computerized Medical Imaging and Graphics. 32(3). 221–238. 5 indexed citations
15.
McKenna, S.J., et al.. (2007). Learning Active Shape Models for Bifurcating Contours. IEEE Transactions on Medical Imaging. 26(5). 666–677. 15 indexed citations
16.
Bengough, A. Glyn, M. F. Bransby, Joachim Hans, et al.. (2005). Root responses to soil physical conditions; growth dynamics from field to cell. Journal of Experimental Botany. 57(2). 437–447. 409 indexed citations
17.
McKenna, S.J., et al.. (2003). Scenario-based Drama as a Tool for Investigating User Requirements with Application to Home Monitoring for Elderly People. Discovery Research Portal (University of Dundee). 512–516. 17 indexed citations
18.
McKenna, S.J., Shaogang Gong, & Yogesh Raja. (1997). Face recognition in dynamic scenes. British Machine Vision Conference. 34. 429–36. 27 indexed citations
19.
Rolfe, P., et al.. (1995). In vivo assessment of catheter-tip PO2 sensor: sampling lumen fabrication. Medical & Biological Engineering & Computing. 33(2). 157–162. 2 indexed citations
20.
McKenna, S.J., et al.. (1993). A comparison of neural network architectures for cervical cell classification. 105–109. 9 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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