Dyke Ferber
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- AI in cancer detection 8
- Topic Modeling 3
- Machine Learning in Healthcare 2
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- Artificial Intelligence in Healthcare and Education 8
- Co-authors
- Jakob Nikolas Kather (22 shared papers)Dirk Jäger (6 shared papers)Niels Halama (4 shared papers)Inka Zörnig (3 shared papers)Michael Hoffmeister (2 shared papers)Hermann Brenner (2 shared papers)Jenny Chang‐Claude (2 shared papers)Alexander Marx (2 shared papers)
- Journals
- npj Precision Oncology (3 papers)npj Digital Medicine (3 papers)Nature Communications (2 papers)Nature Cancer (2 papers)OncoImmunology (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Dyke Ferber
23 papers receiving 1.2k citations
Dyke Ferber's Hit Papers
Peers
Comparison fields: 5 of 92
- Health Informatics 131
- Radiology, Nuclear Medicine and Imaging 427
- Artificial Intelligence 549
- Oncology 314
- Biophysics 63
Countries citing papers authored by Dyke Ferber
This map shows the geographic impact of Dyke Ferber'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 Dyke Ferber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dyke Ferber more than expected).
Fields of papers citing papers by Dyke Ferber
This network shows the impact of papers produced by Dyke Ferber. 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 Dyke Ferber. The network helps show where Dyke Ferber may publish in the future.
Co-authors
The 25 scholars most cited alongside Dyke Ferber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study Hit paper breakdown → | 2019 | 643 |
| 2 | 2018 | 204 | |
| 3 | In-context learning enables multimodal large language models to classify cancer pathology images Hit paper breakdown → | 2024 | 61 |
| 4 | 2024 | 51 | |
| 5 | 2024 | 42 | |
| 6 | Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology Hit paper breakdown → | 2025 | 41 |
| 7 | 2024 | 32 | |
| 8 | 2024 | 25 | |
| 9 | 2024 | 16 | |
| 10 | 2018 | 15 | |
| 11 | 2025 | 14 | |
| 12 | 2025 | 11 | |
| 13 | 2024 | 9 | |
| 14 | 2025 | 8 | |
| 15 | 2024 | 8 | |
| 16 | 2024 | 5 | |
| 17 | 2024 | 5 | |
| 18 | 2025 | 3 | |
| 19 | 2025 | 2 | |
| 20 | 2025 | 1 |
About Dyke Ferber
Dyke Ferber is a scholar working on Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Oncology, having authored 25 papers that have together received 1.2k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (8 papers), AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Topic Modeling (3 papers), Biomedical Text Mining and Ontologies (3 papers), Immunotherapy and Immune Responses (2 papers), Machine Learning in Healthcare (2 papers) and Cancer Immunotherapy and Biomarkers (2 papers). The work is most often cited by research in Health Informatics (131 citations), Radiology, Nuclear Medicine and Imaging (427 citations), Artificial Intelligence (549 citations), Oncology (314 citations) and Biophysics (63 citations). Dyke Ferber has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Jakob Nikolas Kather, Dirk Jäger, Niels Halama, Inka Zörnig, Michael Hoffmeister, Hermann Brenner, Jenny Chang‐Claude, Alexander Marx, Pornpimol Charoentong and Cleo‐Aron Weis. Their work appears in journals such as npj Precision Oncology, npj Digital Medicine, Nature Communications, Nature Cancer and OncoImmunology.
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.