Sarah Parisot

31 papers and 2.0k indexed citations i.

About

Sarah Parisot is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Sarah Parisot has authored 31 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 13 papers in Radiology, Nuclear Medicine and Imaging and 11 papers in Artificial Intelligence. Recurrent topics in Sarah Parisot’s work include Functional Brain Connectivity Studies (9 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Multimodal Machine Learning Applications (6 papers). Sarah Parisot is often cited by papers focused on Functional Brain Connectivity Studies (9 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Multimodal Machine Learning Applications (6 papers). Sarah Parisot collaborates with scholars based in United Kingdom, France and United States. Sarah Parisot's co-authors include Daniel Rueckert, Greg Slabaugh, Sofia Ira Ktena, Aleš Leonardis, Tinne Tuytelaars, Rahaf Aljundi, Marc Masana, Xu Jia, Enzo Ferrante and Ben Glocker and has published in prestigious journals such as PLoS ONE, NeuroImage and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Co-authorship network of co-authors of Sarah Parisot i

Fields of papers citing papers by Sarah Parisot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Sarah Parisot

Since Specialization
Citations

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

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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