Fabian Eitel

551 total citations
9 papers, 231 citations indexed

About

Fabian Eitel is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Fabian Eitel has authored 9 papers receiving a total of 231 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Cognitive Neuroscience and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Fabian Eitel's work include Machine Learning in Healthcare (4 papers), Functional Brain Connectivity Studies (4 papers) and Advanced Neuroimaging Techniques and Applications (3 papers). Fabian Eitel is often cited by papers focused on Machine Learning in Healthcare (4 papers), Functional Brain Connectivity Studies (4 papers) and Advanced Neuroimaging Techniques and Applications (3 papers). Fabian Eitel collaborates with scholars based in Germany, United States and Netherlands. Fabian Eitel's co-authors include Kerstin Ritter, Martin Weygandt, Moritz Böhle, Marc-André Schulz, Henrik Walter, Friedemann Paul, Tanja Schmitz‐Hübsch, Mohamad Habes, Stefan Hetzer and Claudia Chien and has published in prestigious journals such as Scientific Reports, Experimental Neurology and Frontiers in Aging Neuroscience.

In The Last Decade

Fabian Eitel

9 papers receiving 226 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fabian Eitel Germany	5	114	50	48	40	37	9	231
Moritz Böhle Germany	4	133 1.2×	40 0.8×	34 0.7×	34 0.8×	29 0.8×	8	220
Nicola K. Dinsdale United Kingdom	7	84 0.7×	106 2.1×	79 1.6×	25 0.6×	39 1.1×	16	270
Paul Hager Germany	6	110 1.0×	85 1.7×	40 0.8×	98 2.5×	31 0.8×	9	345
Lijun An Singapore	5	73 0.6×	59 1.2×	99 2.1×	7 0.2×	35 0.9×	9	238
Eufemia Lella Italy	10	60 0.5×	86 1.7×	96 2.0×	9 0.2×	69 1.9×	14	262
Marc-André Schulz Germany	7	66 0.6×	62 1.2×	136 2.8×	15 0.4×	15 0.4×	11	298
Joana Câmara Portugal	6	44 0.4×	28 0.6×	30 0.6×	14 0.3×	64 1.7×	12	276
Qiankun Zuo China	8	67 0.6×	52 1.0×	92 1.9×	5 0.1×	52 1.4×	26	266
Seungjun Ryu South Korea	8	74 0.6×	77 1.5×	110 2.3×	9 0.2×	95 2.6×	29	346

Countries citing papers authored by Fabian Eitel

Since Specialization

Citations

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

Fields of papers citing papers by Fabian Eitel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabian Eitel

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

All Works

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9 of 9 papers shown

Eitel, Fabian, et al.. (2024). Benchmarking the influence of pre-training on explanation performance in MR image classification. Frontiers in Artificial Intelligence. 7. 1330919–1330919. 4 indexed citations

Schulz, Marc-André, Stefan Hetzer, Fabian Eitel, et al.. (2023). Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis. iScience. 26(9). 107679–107679. 5 indexed citations

Eitel, Fabian, et al.. (2023). Higher performance for women than men in MRI-based Alzheimer’s disease detection. Alzheimer s Research & Therapy. 15(1). 84–84. 13 indexed citations

Chien, Claudia, et al.. (2022). Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity. Multiple Sclerosis Journal - Experimental Translational and Clinical. 8(3). 3090472970–3090472970. 6 indexed citations

Eitel, Fabian, et al.. (2021). Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data. Scientific Reports. 11(1). 24447–24447. 4 indexed citations

Eitel, Fabian, et al.. (2021). Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Experimental Neurology. 339. 113608–113608. 32 indexed citations

Eitel, Fabian, Kerstin Ritter, Stefan Hetzer, et al.. (2020). Altered Coupling of Psychological Relaxation and Regional Volume of Brain Reward Areas in Multiple Sclerosis. Frontiers in Neurology. 11. 568850–568850. 3 indexed citations

Böhle, Moritz, Fabian Eitel, Martin Weygandt, & Kerstin Ritter. (2019). Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification. Frontiers in Aging Neuroscience. 11. 194–194. 162 indexed citations

Böhle, Moritz, Fabian Eitel, Martin Weygandt, & Kerstin Ritter. (2019). Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data. 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.

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