Virginie Uhlmann

990 total citations
37 papers, 402 citations indexed

About

Virginie Uhlmann is a scholar working on Biophysics, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Virginie Uhlmann has authored 37 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Biophysics, 16 papers in Computer Vision and Pattern Recognition and 12 papers in Molecular Biology. Recurrent topics in Virginie Uhlmann's work include Cell Image Analysis Techniques (21 papers), Image Processing Techniques and Applications (8 papers) and Image and Signal Denoising Methods (6 papers). Virginie Uhlmann is often cited by papers focused on Cell Image Analysis Techniques (21 papers), Image Processing Techniques and Applications (8 papers) and Image and Signal Denoising Methods (6 papers). Virginie Uhlmann collaborates with scholars based in Switzerland, United Kingdom and United States. Virginie Uhlmann's co-authors include Michaël Unser, Ricard Delgado-Gonzalo, Daniel Schmitter, Shantanu Singh, Anne E. Carpenter, Adrien Hallou, Hannah Yevick, Bianca Dumitrascu, Julien Fageot and Richard Benton and has published in prestigious journals such as The EMBO Journal, Bioinformatics and PLoS ONE.

In The Last Decade

Virginie Uhlmann

36 papers receiving 395 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Virginie Uhlmann Switzerland 12 159 135 94 70 61 37 402
Brigitte Forster Germany 10 62 0.4× 215 1.6× 63 0.7× 153 2.2× 51 0.8× 38 527
Jesse Berent United Kingdom 8 60 0.4× 254 1.9× 62 0.7× 131 1.9× 50 0.8× 14 501
K. Rohr Germany 13 216 1.4× 156 1.2× 134 1.4× 77 1.1× 11 0.2× 39 479
Hagai Kirshner Switzerland 9 438 2.8× 99 0.7× 119 1.3× 88 1.3× 62 1.0× 23 669
Baris Sumengen United States 11 87 0.5× 364 2.7× 52 0.6× 94 1.3× 19 0.3× 24 499
Catherine F. Higham United Kingdom 8 34 0.2× 49 0.4× 170 1.8× 60 0.9× 66 1.1× 14 489
Rafael Redondo Spain 11 80 0.5× 267 2.0× 28 0.3× 250 3.6× 25 0.4× 21 516
Yuri Murakami Japan 16 48 0.3× 270 2.0× 49 0.5× 153 2.2× 14 0.2× 65 622
Sharmishtaa Seshamani United States 10 241 1.5× 115 0.9× 150 1.6× 91 1.3× 7 0.1× 24 733
Kenong Wu Canada 7 102 0.6× 170 1.3× 21 0.2× 51 0.7× 21 0.3× 13 297

Countries citing papers authored by Virginie Uhlmann

Since Specialization
Citations

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

Fields of papers citing papers by Virginie Uhlmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Virginie Uhlmann

This figure shows the co-authorship network connecting the top 25 collaborators of Virginie Uhlmann. A scholar is included among the top collaborators of Virginie Uhlmann 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 Virginie Uhlmann. Virginie Uhlmann 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.
Woodhams, Benjamin & Virginie Uhlmann. (2025). From images to understanding: Advances in deep learning for cellular dynamics analysis. Current Opinion in Cell Biology. 97. 102585–102585.
2.
Uhlmann, Virginie, Matthew Hartley, Josh Moore, Erin Weisbart, & Assaf Zaritsky. (2024). Making the most of bioimaging data through interdisciplinary interactions. Journal of Cell Science. 137(20). 1 indexed citations
3.
4.
Bondarenko, Vladyslav, Mikhail Nikolaev, Roman Belousov, et al.. (2023). Embryo‐uterine interaction coordinates mouse embryogenesis during implantation. The EMBO Journal. 42(17). e113280–e113280. 20 indexed citations
5.
Culley, S J, et al.. (2023). Made to measure: An introduction to quantifying microscopy data in the life sciences. Journal of Microscopy. 295(1). 61–82. 9 indexed citations
6.
Senft, Rebecca A., Barbara Diaz‐Rohrer, Pina Colarusso, et al.. (2023). A biologist's guide to the field of quantitative bioimaging. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
7.
Senft, Rebecca A., Barbara Diaz‐Rohrer, Pina Colarusso, et al.. (2023). A biologist’s guide to planning and performing quantitative bioimaging experiments. PLoS Biology. 21(6). e3002167–e3002167. 11 indexed citations
8.
Theiß, M., Jean-Karim Hèriché, Jonas Ries, et al.. (2023). Simulating structurally variable nuclear pore complexes for microscopy. Bioinformatics. 39(10). 2 indexed citations
9.
Fageot, Julien, Virginie Uhlmann, Zsuzsanna Püspöki, et al.. (2021). Principled Design and Implementation of Steerable Detectors. SERVAL (Université de Lausanne). 3 indexed citations
10.
Lundberg, Emma, Jan Funke, Virginie Uhlmann, et al.. (2021). Which image-based phenotypes are most promising for using AI to understand cellular functions and why?. Cell Systems. 12(5). 384–387. 1 indexed citations
11.
Nguyen, David, et al.. (2019). Supervised learning to quantify amyloidosis in whole brains of an Alzheimer’s disease mouse model acquired with optical projection tomography. Biomedical Optics Express. 10(6). 3041–3041. 10 indexed citations
12.
Fageot, Julien, Virginie Uhlmann, & Michaël Unser. (2018). Gaussian and sparse processes are limits of generalized Poisson processes. Applied and Computational Harmonic Analysis. 48(3). 1045–1065. 4 indexed citations
13.
Uhlmann, Virginie, Carsten Haubold, Fred A. Hamprecht, & Michaël Unser. (2017). DiversePathsJ: diverse shortest paths for bioimage analysis. Bioinformatics. 34(3). 538–540. 2 indexed citations
14.
Uhlmann, Virginie, Pavan P Ramdya, Ricard Delgado-Gonzalo, Richard Benton, & Michaël Unser. (2017). FlyLimbTracker: An active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila. PLoS ONE. 12(4). e0173433–e0173433. 27 indexed citations
15.
Uhlmann, Virginie, Shantanu Singh, & Anne E. Carpenter. (2016). CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics. 17(1). 51–51. 42 indexed citations
16.
Uhlmann, Virginie, Julien Fageot, & Michaël Unser. (2016). Hermite Snakes With Control of Tangents. IEEE Transactions on Image Processing. 25(6). 2803–2816. 14 indexed citations
17.
Schmitter, Daniel, et al.. (2016). Multiresolution Subdivision Snakes. IEEE Transactions on Image Processing. 26(3). 1188–1201. 17 indexed citations
18.
Uhlmann, Virginie, Julien Fageot, Harshit Gupta, & Michaël Unser. (2015). Statistical optimality of Hermite splines. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 226–230. 2 indexed citations
19.
Delgado-Gonzalo, Ricard, Daniel Schmitter, Virginie Uhlmann, & Michaël Unser. (2015). Efficient Shape Priors for Spline-Based Snakes. IEEE Transactions on Image Processing. 24(11). 3915–3926. 14 indexed citations
20.
Uhlmann, Virginie, P. Palanisamy, Caroline Kampf, et al.. (2013). Automated classification of immunostaining patterns in breast tissue from the human protein atlas. Journal of Pathology Informatics. 4(2). 14–14. 17 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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