Meyke Hermsen
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 2%
- Pulmonary and Respiratory Medicine top 10%
- Oncology
- Co-authors
- Jeroen van der LaakGeert LitjensPeter BultBram van GinnekenN. K. TimofeevaClara I. SánchezIrıs D. NagtegaalIringo Kovacs
- Topics
- AI in cancer detection (15 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)Digital Imaging for Blood Diseases (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsJournal of the American Society of Nephrology
- Partner nations
- NetherlandsUnited StatesSweden
In The Last Decade
Meyke Hermsen
20 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Artificial Intelligence 1.1k
- Radiology, Nuclear Medicine and Imaging 734
- Computer Vision and Pattern Recognition 428
- Pulmonary and Respiratory Medicine 227
- Oncology 225
Countries citing papers authored by Meyke Hermsen
This map shows the geographic impact of Meyke Hermsen'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 Meyke Hermsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Meyke Hermsen more than expected).
Fields of papers citing papers by Meyke Hermsen
This network shows the impact of papers produced by Meyke Hermsen. 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 Meyke Hermsen. The network helps show where Meyke Hermsen may publish in the future.
Co-authorship network of co-authors of Meyke Hermsen
This figure shows the co-authorship network connecting the top 25 collaborators of Meyke Hermsen. A scholar is included among the top collaborators of Meyke Hermsen 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 Meyke Hermsen. Meyke Hermsen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 38 | |
| 3 | 21 | |
| 4 | 4 | |
| 5 | 9 | |
| 6 | 0 | |
| 7 | 30 | |
| 8 | 24 | |
| 9 | 8 | |
| 10 | Deep Learning-Based Histopathologic Assessment of Kidney Tissue | 3 |
| 11 | 235 | |
| 12 | Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology | 47 |
| 13 | 216 | |
| 14 | 47 | |
| 15 | 9 | |
| 16 | 15 | |
| 17 | 130 | |
| 18 | 8 | |
| 19 | Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosisbreakdown → | 722 |
| 20 | 2 |
About Meyke Hermsen
Meyke Hermsen is a scholar working on Health Informatics, Artificial Intelligence and Transplantation, having authored 22 papers that have together received 1.6k indexed citations. Recurring topics across this work include AI in cancer detection (15 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Digital Imaging for Blood Diseases (3 papers). The work is most often cited by research in Health Informatics (125 citations), Biophysics (198 citations) and Artificial Intelligence (1.1k citations). Meyke Hermsen has collaborated with scholars based in Netherlands, United States and Sweden. Frequent co-authors include Jeroen van der Laak, Geert Litjens, Peter Bult, Bram van Ginneken, N. K. Timofeeva, Clara I. Sánchez, Irıs D. Nagtegaal, Iringo Kovacs, Babak Ehteshami Bejnordi and Thomas de Bel. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of the American Society of Nephrology.
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.