John‐Melle Bokhorst
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Oncology
- Biophysics top 5%
- Co-authors
- Jeroen van der LaakGeert LitjensFrancesco CiompiPéter BándiDavid TellezWouter BultenIrıs D. NagtegaalThomas de Bel
- Topics
- Radiomics and Machine Learning in Medical Imaging (13 papers)AI in cancer detection (12 papers)Colorectal Cancer Screening and Detection (9 papers)
- Partner nations
- NetherlandsSwitzerlandSweden
In The Last Decade
John‐Melle Bokhorst
21 papers receiving 607 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 402
- Radiology, Nuclear Medicine and Imaging 282
- Computer Vision and Pattern Recognition 201
- Oncology 192
- Biophysics 74
Countries citing papers authored by John‐Melle Bokhorst
This map shows the geographic impact of John‐Melle Bokhorst'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 John‐Melle Bokhorst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John‐Melle Bokhorst more than expected).
Fields of papers citing papers by John‐Melle Bokhorst
This network shows the impact of papers produced by John‐Melle Bokhorst. 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 John‐Melle Bokhorst. The network helps show where John‐Melle Bokhorst may publish in the future.
Co-authorship network of co-authors of John‐Melle Bokhorst
This figure shows the co-authorship network connecting the top 25 collaborators of John‐Melle Bokhorst. A scholar is included among the top collaborators of John‐Melle Bokhorst 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 John‐Melle Bokhorst. John‐Melle Bokhorst 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 | 4 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | 15 | |
| 10 | 12 | |
| 11 | 26 | |
| 12 | 15 | |
| 13 | 12 | |
| 14 | 40 | |
| 15 | 59 | |
| 16 | 19 | |
| 17 | 14 | |
| 18 | 34 | |
| 19 | Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologybreakdown → | 317 |
| 20 | 14 |
About John‐Melle Bokhorst
John‐Melle Bokhorst is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Artificial Intelligence, having authored 22 papers that have together received 617 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (13 papers), AI in cancer detection (12 papers) and Colorectal Cancer Screening and Detection (9 papers). The work is most often cited by research in Health Informatics (21 citations), Biophysics (74 citations) and Artificial Intelligence (402 citations). John‐Melle Bokhorst has collaborated with scholars based in Netherlands, Switzerland and Sweden. Frequent co-authors include Jeroen van der Laak, Geert Litjens, Francesco Ciompi, Péter Bándi, David Tellez, Wouter Bulten, Irıs D. Nagtegaal, Thomas de Bel, Alessandro Lugli and Inti Zlobec. Their work appears in journals such as PLoS ONE, Scientific Reports and European Journal of Cancer.
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.