Senan Doyle

6.9k total citations
10 papers, 131 citations indexed

About

Senan Doyle is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Health Informatics. According to data from OpenAlex, Senan Doyle has authored 10 papers receiving a total of 131 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Networks and Communications, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Health Informatics. Recurrent topics in Senan Doyle's work include Machine Learning in Healthcare (2 papers), Opportunistic and Delay-Tolerant Networks (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Senan Doyle is often cited by papers focused on Machine Learning in Healthcare (2 papers), Opportunistic and Delay-Tolerant Networks (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). Senan Doyle collaborates with scholars based in France, Ireland and Switzerland. Senan Doyle's co-authors include Florence Forbes, Michel Dojat, Daniel García-Lorenzo, Christian Barillot, Linda Doyle, Alan Tucholka, Anil Kokaram, David Abrial, Lamiae Azizi and Alexandre Krainik and has published in prestigious journals such as IEEE Signal Processing Magazine, International Journal of Applied Earth Observation and Geoinformation and Frontiers in Neurology.

In The Last Decade

Senan Doyle

9 papers receiving 125 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Senan Doyle France	6	45	35	34	21	15	10	131
Parneet Singh India	5	138 3.1×	76 2.2×	16 0.5×	13 0.6×	8 0.5×	23	366
Soumya Surath Panda India	5	105 2.3×	102 2.9×	34 1.0×	63 3.0×	14 0.9×	31	233
Zhu Haisheng China	4	48 1.1×	71 2.0×	24 0.7×	31 1.5×	3 0.2×	7	194
Sorina Camarasu-Pop France	6	21 0.5×	88 2.5×	40 1.2×	23 1.1×	14 0.9×	15	219
Yizhi Pan China	6	46 1.0×	30 0.9×	56 1.6×	34 1.6×	7 0.5×	12	220
Sonit Singh Australia	8	105 2.3×	85 2.4×	64 1.9×	25 1.2×	3 0.2×	19	202
Sunil Kumar Yadav Germany	10	19 0.4×	63 1.8×	56 1.6×	12 0.6×	48 3.2×	28	256
Siva Skandha Sanagala India	8	45 1.0×	80 2.3×	25 0.7×	17 0.8×	2 0.1×	11	210
Donghoon Yu South Korea	5	35 0.8×	107 3.1×	41 1.2×	7 0.3×	11 0.7×	17	295
Ruchi Singla India	7	80 1.8×	69 2.0×	30 0.9×	13 0.6×	2 0.1×	12	236

Countries citing papers authored by Senan Doyle

Since Specialization

Citations

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

Fields of papers citing papers by Senan Doyle

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Senan Doyle

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

All Works

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10 of 10 papers shown

Forbes, Florence, et al.. (2024). Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis. Artificial Intelligence in Medicine. 150. 102830–102830. 63 indexed citations

Roca, Pauline, Alan Tucholka, Florence Forbes, et al.. (2022). Automated Quantification of Brain Lesion Volume From Post-trauma MR Diffusion-Weighted Images. Frontiers in Neurology. 12. 740603–740603.

Forbes, Florence, et al.. (2022). Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis. arXiv (Cornell University). 10 indexed citations

Bonnan, Mickaël, et al.. (2020). Focal cortical atrophy following transient meningeal enhancement in a progressive multiple sclerosis. Neurological Sciences. 42(5). 1959–1961. 2 indexed citations

Doyle, Senan, Florence Forbes, & Michel Dojat. (2016). Automatic multiple sclerosis lesion segmentation with P-LOCUS. HAL (Le Centre pour la Communication Scientifique Directe). 17–21. 2 indexed citations

Azizi, Lamiae, et al.. (2012). On the difficulty to delimit disease risk hot spots. International Journal of Applied Earth Observation and Geoinformation. 22. 99–105. 7 indexed citations

Forbes, Florence, Senan Doyle, Daniel García-Lorenzo, Christian Barillot, & Michel Dojat. (2010). A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation. HAL (Le Centre pour la Communication Scientifique Directe). 9. 225–232. 9 indexed citations

Forbes, Florence, Senan Doyle, Daniel García-Lorenzo, Christian Barillot, & Michel Dojat. (2010). Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation. HAL (Le Centre pour la Communication Scientifique Directe). 69–72. 28 indexed citations

Doyle, Linda, et al.. (2006). Ad hoc networking, markov random fields, and decision making. IEEE Signal Processing Magazine. 23(5). 63–73. 8 indexed citations

10.

Doyle, Senan, et al.. (2006). Spatial Stationarity of Link Statistics in Mobile Ad Hoc Network Modelling. 10. 43–50. 2 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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