Rob van de Loo
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Biophysics top 5%
- Cell Image Analysis Techniques
Papers in
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- Digital Imaging for Blood Diseases 1
- Medical Image Segmentation Techniques 1
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- AI in cancer detection 2
- Co-authors
- Jeroen van der Laak (4 shared papers)Geert Litjens (4 shared papers)Péter Bándi (3 shared papers)Peter Bult (2 shared papers)Maschenka Balkenhol (2 shared papers)Marcory van Dijk (1 shared paper)Bram van Ginneken (2 shared papers)Paul van Diest (1 shared paper)
- Journals
- The Breast (1 paper)Journal of Clinical Pathology (1 paper)GigaScience (1 paper)Scientific Reports (1 paper)
- Partner nations
- NetherlandsGermanyPoland
In The Last Decade
Rob van de Loo
5 papers receiving 423 citations
Rob van de Loo's Hit Papers
Peers
Comparison fields: 5 of 47
- Health Informatics 25
- Biophysics 74
- Radiology, Nuclear Medicine and Imaging 207
- Artificial Intelligence 331
- Computer Vision and Pattern Recognition 132
Countries citing papers authored by Rob van de Loo
This map shows the geographic impact of Rob van de Loo'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 Rob van de Loo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rob van de Loo more than expected).
Fields of papers citing papers by Rob van de Loo
This network shows the impact of papers produced by Rob van de Loo. 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 Rob van de Loo. The network helps show where Rob van de Loo may publish in the future.
Co-authors
The 25 scholars most cited alongside Rob van de Loo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset Hit paper breakdown → | 2018 | 255 |
| 2 | 2019 | 109 | |
| 3 | 2017 | 31 | |
| 4 | 2021 | 25 | |
| 5 | 2017 | 10 |
About Rob van de Loo
Rob van de Loo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Pulmonary and Respiratory Medicine, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 5 papers that have together received 430 indexed citations. Recurring topics across this work include AI in cancer detection (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Cancer Immunotherapy and Biomarkers (1 paper), Digital Imaging for Blood Diseases (1 paper), Medical Image Segmentation Techniques (1 paper), Prostate Cancer Diagnosis and Treatment (1 paper), Immunotherapy and Immune Responses (1 paper) and Immune cells in cancer (1 paper). The work is most often cited by research in Health Informatics (25 citations), Biophysics (74 citations), Radiology, Nuclear Medicine and Imaging (207 citations), Artificial Intelligence (331 citations) and Computer Vision and Pattern Recognition (132 citations). Rob van de Loo has collaborated with scholars based in Netherlands, Germany and Poland. Frequent co-authors include Jeroen van der Laak, Geert Litjens, Péter Bándi, Peter Bult, Maschenka Balkenhol, Marcory van Dijk, Bram van Ginneken, Paul van Diest, Altuna Halilović and Oscar Geessink. Their work appears in journals such as The Breast, Journal of Clinical Pathology, GigaScience and Scientific Reports.
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.