Ferdinand Seith

683 citations
42 papers · 507 · h-index 14

Impact in

    • Medical Imaging Techniques and Applications
    • Radiomics and Machine Learning in Medical Imaging
    • Advanced MRI Techniques and Applications
    • MRI in cancer diagnosis
    • Cancer Immunotherapy and Biomarkers

Papers in

Ferdinand Seith

38 papers receiving 502 citations

Peers

Ferdinand Seith
Comparison fields: 5 of 50
  • Radiology, Nuclear Medicine and Imaging 283
  • Oncology 132
  • Radiation 37
  • Pulmonary and Respiratory Medicine 61
  • Neurology 28
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Citations per year

Countries citing papers authored by Ferdinand Seith

Since Specialization
Citations

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

Fields of papers citing papers by Ferdinand Seith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ferdinand Seith, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ferdinand Seith Line = papers co-authored together Ferdinand Seith links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201759
2 201946
3 201744
4 201633
5 201631
6 201728
7 201827
8 202026
9 201823
10 202121
11 201519
12 201716
13 202014
14 201613
15 202311
16 201710
17 20229
18 20239
19 20228
20 20238

About Ferdinand Seith

Ferdinand Seith is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Oncology, Epidemiology and Immunology, having authored 42 papers that have together received 507 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Medical Imaging Techniques and Applications (6 papers), Cancer Immunotherapy and Biomarkers (5 papers), Advanced MRI Techniques and Applications (5 papers), Prostate Cancer Diagnosis and Treatment (3 papers), Prostate Cancer Treatment and Research (3 papers) and Immunotherapy and Immune Responses (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (283 citations), Oncology (132 citations), Radiation (37 citations), Pulmonary and Respiratory Medicine (61 citations) and Neurology (28 citations). Ferdinand Seith has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Konstantin Nikolaou, Nina F. Schwenzer, Christian la Fougère, Holger Schmidt, Christina Pfannenberg, Sergios Gatidis, Thomas Küstner, Petros Martirosian, Martin Schwartz and Andrea Forschner. Their work appears in journals such as RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Theranostics, Investigative Radiology, Korean Journal of Radiology and Journal of Clinical Medicine.

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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