Hans Pinckaers
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Computer Vision and Pattern Recognition top 5%
- Oncology
- Co-authors
- Geert LitjensJeroen van der LaakBram van GinnekenWouter BultenRobert VinkHester van BovenThomas de BelChristina Hulsbergen‐van de Kaa
- Topics
- AI in cancer detection (14 papers)Radiomics and Machine Learning in Medical Imaging (9 papers)Prostate Cancer Diagnosis and Treatment (7 papers)
- Journals
- Journal of Clinical OncologySHILAP Revista de lepidopterologíaPLoS ONE
- Partner nations
- NetherlandsUnited StatesSweden
In The Last Decade
Hans Pinckaers
20 papers receiving 890 citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Artificial Intelligence 608
- Radiology, Nuclear Medicine and Imaging 477
- Pulmonary and Respiratory Medicine 217
- Computer Vision and Pattern Recognition 183
- Oncology 150
Countries citing papers authored by Hans Pinckaers
This map shows the geographic impact of Hans Pinckaers'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 Hans Pinckaers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hans Pinckaers more than expected).
Fields of papers citing papers by Hans Pinckaers
This network shows the impact of papers produced by Hans Pinckaers. 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 Hans Pinckaers. The network helps show where Hans Pinckaers may publish in the future.
Co-authorship network of co-authors of Hans Pinckaers
This figure shows the co-authorship network connecting the top 25 collaborators of Hans Pinckaers. A scholar is included among the top collaborators of Hans Pinckaers 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 Hans Pinckaers. Hans Pinckaers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 9 | |
| 3 | 2 | |
| 4 | 23 | |
| 5 | 1 | |
| 6 | 31 | |
| 7 | 15 | |
| 8 | 4 | |
| 9 | Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study | 77 |
| 10 | 58 | |
| 11 | 14 | |
| 12 | Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic studybreakdown → | 421 |
| 13 | 73 | |
| 14 | 104 | |
| 15 | 1 | |
| 16 | 15 | |
| 17 | Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images | 10 |
| 18 | Learning from sparsely annotated data for semantic segmentation in histopathology images | 12 |
| 19 | 26 | |
| 20 | 1 |
About Hans Pinckaers
Hans Pinckaers is a scholar working on Family Practice, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 20 papers that have together received 899 indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Prostate Cancer Diagnosis and Treatment (7 papers). The work is most often cited by research in Health Informatics (88 citations), Radiology, Nuclear Medicine and Imaging (477 citations) and Artificial Intelligence (608 citations). Hans Pinckaers has collaborated with scholars based in Netherlands, United States and Sweden. Frequent co-authors include Geert Litjens, Jeroen van der Laak, Bram van Ginneken, Wouter Bulten, Robert Vink, Hester van Boven, Thomas de Bel, Christina Hulsbergen‐van de Kaa, Francesco Ciompi and Maschenka Balkenhol. Their work appears in journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.
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.