Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
TeleOphta: Machine learning and image processing methods for teleophthalmology
2013343 citationsÉtienne Decencière, Guy Cazuguel et al.IRBMprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Ronan Danno'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 Ronan Danno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ronan Danno more than expected).
This network shows the impact of papers produced by Ronan Danno. 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 Ronan Danno. The network helps show where Ronan Danno may publish in the future.
Co-authorship network of co-authors of Ronan Danno
This figure shows the co-authorship network connecting the top 25 collaborators of Ronan Danno.
A scholar is included among the top collaborators of Ronan Danno 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 Ronan Danno. Ronan Danno is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
11 of 11 papers shown
1.
Laÿ, Bruno, Ronan Danno, Gwenolé Quellec, et al.. (2020). Using Artificial Intelligence to detect glaucoma and Age related Macula Degeneration. Investigative Ophthalmology & Visual Science. 61(7). 1647–1647.1 indexed citations
2.
Normand, Guillaume, Gwenolé Quellec, Ronan Danno, et al.. (2019). Prediction of Geographic Atrophy progression by deep learning applied to retinal imaging. 60(9). 1452–1452.2 indexed citations
3.
Laÿ, Bruno, et al.. (2018). Repeatability and Validation of Scheimpflug Scleral Data. 59(9). 1774–1774.3 indexed citations
Decencière, Étienne, Guy Cazuguel, Guillaume Thibault, et al.. (2013). Iconography : TeleOphta: Machine learning and image processing methods for teleophthalmology.2 indexed citations
10.
Decencière, Étienne, Guy Cazuguel, Xiaofeng Zhang, et al.. (2013). TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM. 34(2). 196–203.343 indexed citations breakdown →
11.
Zhang, Xiwei, Guillaume Thibault, Étienne Decencière, et al.. (2012). Automatic Detection Of Exudates In Color Retinal Images. 53(14). 2083–2083.2 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.