Dyke Ferber

2.1k total citations · 4 hit papers
23 papers, 1.1k citations indexed

About

Dyke Ferber is a scholar working on Artificial Intelligence, Health Informatics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Dyke Ferber has authored 23 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 9 papers in Health Informatics and 9 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Dyke Ferber's work include Artificial Intelligence in Healthcare and Education (9 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and AI in cancer detection (8 papers). Dyke Ferber is often cited by papers focused on Artificial Intelligence in Healthcare and Education (9 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and AI in cancer detection (8 papers). Dyke Ferber collaborates with scholars based in Germany, United Kingdom and United States. Dyke Ferber's co-authors include Jakob Nikolas Kather, Dirk Jäger, Niels Halama, Timo Gaiser, Michael Hoffmeister, Hermann Brenner, Inka Zörnig, Jenny Chang‐Claude, Cleo‐Aron Weis and Esther Herpel and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and The British Journal of Psychiatry.

In The Last Decade

Dyke Ferber

21 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

2019 611 citations Jakob Nikolas Kather, Johannes Krisam et al. PLoS Medicine profile →
In-context learning enables multimodal large language models to classify cancer pathology images

2024 47 citations Dyke Ferber, Georg Wölflein et al. Nature Communications profile →
GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines

2024 47 citations Dyke Ferber, Isabella C. Wiest et al. NEJM AI profile →
Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology

2025 18 citations Dyke Ferber, Omar S. M. El Nahhas et al. Nature Cancer profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dyke Ferber Germany	11	579	507	386	138	133	23	1.1k
Dmitrii Bychkov Finland	10	375 0.6×	369 0.7×	219 0.6×	72 0.5×	56 0.4×	19	830
Balázs Ács Sweden	17	436 0.8×	461 0.9×	563 1.5×	70 0.5×	65 0.5×	50	1.2k
Thomas Clozel United States	13	439 0.8×	463 0.9×	265 0.7×	72 0.5×	75 0.6×	18	1.4k
Mark D. Zarella United States	12	681 1.2×	444 0.9×	188 0.5×	199 1.4×	106 0.8×	30	1.1k
Jeremias Krause Germany	3	466 0.8×	489 1.0×	305 0.8×	99 0.7×	64 0.5×	8	868
Richard Colling United Kingdom	18	318 0.5×	263 0.5×	252 0.7×	71 0.5×	97 0.7×	47	769
Andreas Kleppe Norway	11	356 0.6×	460 0.9×	368 1.0×	55 0.4×	84 0.6×	22	1.0k
Narmin Ghaffari Laleh Germany	15	603 1.0×	542 1.1×	260 0.7×	60 0.4×	400 3.0×	20	1.3k
Matahi Moarii France	9	434 0.7×	422 0.8×	215 0.6×	70 0.5×	63 0.5×	12	1.1k
Inka Zörnig Germany	15	469 0.8×	602 1.2×	618 1.6×	131 0.9×	38 0.3×	37	1.4k

Countries citing papers authored by Dyke Ferber

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Dyke Ferber

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Clusmann, Jan, Stefan Schulz, Dyke Ferber, et al.. (2025). Incidental Prompt Injections on Vision–Language Models in Real-Life Histopathology. NEJM AI. 2(6). 3 indexed citations

Wiest, Isabella C., F M Wolf, Dyke Ferber, et al.. (2025). Deidentifying Medical Documents with Local, Privacy-Preserving Large Language Models: The LLM-Anonymizer. NEJM AI. 2(4). 10 indexed citations

Kolbinger, Fiona R., Oliver Lester Saldanha, Steffen Pistorius, et al.. (2025). Vision-language models for automated video analysis and documentation in laparoscopic surgery: a proof-of-concept study. International Journal of Surgery. 111(11). 7777–7786.

Pfarr, Nicole, Tobias Dreyer, Patrick Metzger, et al.. (2025). Large language models-enabled digital twins for precision medicine in rare gynecological tumors. npj Digital Medicine. 8(1). 420–420. 4 indexed citations

Ferber, Dyke, Omar S. M. El Nahhas, Georg Wölflein, et al.. (2025). Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology. Nature Cancer. 6(8). 1337–1349. 18 indexed citations breakdown →

Clusmann, Jan, Dyke Ferber, Isabella C. Wiest, et al.. (2025). Prompt injection attacks on vision language models in oncology. Nature Communications. 16(1). 1239–1239. 9 indexed citations

Wiest, Isabella C., Fabian Wolf, Marko van Treeck, et al.. (2025). A software pipeline for medical information extraction with large language models, open source and suitable for oncology. npj Precision Oncology. 9(1). 313–313.

Wölflein, Georg, Dyke Ferber, Daniel Truhn, Ognjen Arandjelović, & Jakob Nikolas Kather. (2025). LLM Agents Making Agent Tools. 26092–26130. 1 indexed citations

Wiest, Isabella C., Dyke Ferber, Michael Bauer, et al.. (2024). Detection of suicidality from medical text using privacy-preserving large language models. The British Journal of Psychiatry. 225(6). 532–537. 5 indexed citations

10.

Ferber, Dyke, Isabella C. Wiest, Georg Wölflein, et al.. (2024). GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines. NEJM AI. 1(6). 47 indexed citations breakdown →

11.

Inojosa, Hernán, Isabel Voigt, Dyke Ferber, et al.. (2024). Integrating large language models in care, research, and education in multiple sclerosis management. Multiple Sclerosis Journal. 30(11-12). 1392–1401. 7 indexed citations

12.

Wiest, Isabella C., et al.. (2024). Deep sight: enhancing periprocedural adverse event recording in endoscopy by structuring text documentation with privacy-preserving large language models. PubMed. 3(4). 447–452.e5. 4 indexed citations

13.

Dreyer, Tobias, Kai J. Borm, Ulrich A. Schatz, et al.. (2024). Expert-Guided Large Language Models for Clinical Decision Support in Precision Oncology. JCO Precision Oncology. 8(8). e2400478–e2400478. 16 indexed citations

14.

Ferber, Dyke, Georg Wölflein, Isabella C. Wiest, et al.. (2024). In-context learning enables multimodal large language models to classify cancer pathology images. Nature Communications. 15(1). 10104–10104. 47 indexed citations breakdown →

15.

Truhn, Daniel, Jan‐Niklas Eckardt, Dyke Ferber, & Jakob Nikolas Kather. (2024). Large language models and multimodal foundation models for precision oncology. npj Precision Oncology. 8(1). 72–72. 26 indexed citations

16.

Prelaj, Arsela, et al.. (2024). Oncology education in the age of artificial intelligence. SHILAP Revista de lepidopterología. 6. 100079–100079. 4 indexed citations

17.

Wiest, Isabella C., Dyke Ferber, Marko van Treeck, et al.. (2024). Privacy-preserving large language models for structured medical information retrieval. npj Digital Medicine. 7(1). 257–257. 30 indexed citations

18.

Lee, Yong‐Ju, Dyke Ferber, Jennifer Rood, Aviv Regev, & Jakob Nikolas Kather. (2024). How AI agents will change cancer research and oncology. Nature Cancer. 5(12). 1765–1767. 12 indexed citations

19.

Kather, Jakob Nikolas, Johannes Krisam, Pornpimol Charoentong, et al.. (2019). Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine. 16(1). e1002730–e1002730. 611 indexed citations breakdown →

20.

Ferber, Dyke, Meggy Suarez‐Carmona, Sarah Schott, et al.. (2018). Omental fat in ovarian cancer induces lymphangiogenesis. European Journal of Cancer. 92. S8–S8. 1 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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