Stefan Schulz

40 total papers · 602 total citations
17 papers, 355 citations indexed

About

Stefan Schulz is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Stefan Schulz has authored 17 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Molecular Biology. Recurrent topics in Stefan Schulz's work include AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Biomedical Text Mining and Ontologies (4 papers). Stefan Schulz is often cited by papers focused on AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Biomedical Text Mining and Ontologies (4 papers). Stefan Schulz collaborates with scholars based in Germany, Brazil and Chile. Stefan Schulz's co-authors include Wilfried Roth, Sebastian Foersch, Ann-Christin Woerl, Daniel‐Christoph Wagner, Arndt Hartmann, Markus Eckstein, Aurélie Fernandez, Moritz Jesinghaus, Jakob Nikolas Kather and Carol Geppert and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Stefan Schulz

13 papers receiving 344 citations

Hit Papers

Multistain deep learning ... 2023 2026 2024 2023 40 80 120

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Stefan Schulz 164 140 93 62 54 17 355
Jing Hang 173 1.1× 100 0.7× 39 0.4× 54 0.9× 61 1.1× 21 315
Toshiyuki Ishiba 159 1.0× 120 0.9× 67 0.7× 68 1.1× 74 1.4× 35 324
Qiyu Zhao 198 1.2× 135 1.0× 87 0.9× 42 0.7× 20 0.4× 17 324
Junli Shi 104 0.6× 34 0.2× 67 0.7× 69 1.1× 39 0.7× 17 333
Emmanuel Agosto‐Arroyo 147 0.9× 205 1.5× 80 0.9× 39 0.6× 57 1.1× 11 325
David Spak 190 1.2× 149 1.1× 91 1.0× 88 1.4× 71 1.3× 10 327
Carlos F. Villamil 100 0.6× 151 1.1× 52 0.6× 108 1.7× 14 0.3× 19 307
Justinas Besusparis 115 0.7× 146 1.0× 114 1.2× 31 0.5× 112 2.1× 29 369
Carolina Rossi Saccarelli 240 1.5× 106 0.8× 49 0.5× 45 0.7× 55 1.0× 14 297
Shinichi Tsuchiya 53 0.3× 72 0.5× 47 0.5× 73 1.2× 49 0.9× 15 341

Countries citing papers authored by Stefan Schulz

Since Specialization
Citations

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

Fields of papers citing papers by Stefan Schulz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stefan Schulz

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

All Works

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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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