Miriam Hägele

4 papers and 249 indexed citations i.

About

Miriam Hägele is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cancer Research. According to data from OpenAlex, Miriam Hägele has authored 4 papers receiving a total of 249 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Cancer Research. Recurrent topics in Miriam Hägele’s work include Cell Image Analysis Techniques (2 papers), AI in cancer detection (2 papers) and Advanced Neural Network Applications (1 paper). Miriam Hägele is often cited by papers focused on Cell Image Analysis Techniques (2 papers), AI in cancer detection (2 papers) and Advanced Neural Network Applications (1 paper). Miriam Hägele collaborates with scholars based in Germany, Japan and South Korea. Miriam Hägele's co-authors include Wojciech Samek, Philipp Seegerer, Sebastian Lapuschkin, Frederick Klauschen, Klaus‐Robert Müller, Alexander Binder, Michael Bockmayr, Grégoire Montavon, Sven Dähne and K. Müller and has published in prestigious journals such as Scientific Reports, Nature Machine Intelligence and arXiv (Cornell University).

In The Last Decade

Co-authorship network of co-authors of Miriam Hägele i

Fields of papers citing papers by Miriam Hägele

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Miriam Hägele

Since Specialization
Citations

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