Thomas de Bel
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 2%
- AI in cancer detection
Papers in
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- AI in cancer detection 11
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- Radiomics and Machine Learning in Medical Imaging 5
- Co-authors
- Jeroen van der Laak (13 shared papers)Geert Litjens (8 shared papers)Hester van Boven (2 shared papers)Robert Vink (2 shared papers)Bram van Ginneken (2 shared papers)Wouter Bulten (2 shared papers)Hans Pinckaers (2 shared papers)Christina Hulsbergen‐van de Kaa (1 shared paper)
- Journals
- The FASEB Journal (1 paper)Clinical Journal of the American Society of Nephrology (1 paper)Medical Image Analysis (1 paper)Pediatric and Developmental Pathology (1 paper)Cancer Research (1 paper)
- Partner nations
- NetherlandsSwedenUnited States
In The Last Decade
Thomas de Bel
17 papers receiving 1.1k citations
Thomas de Bel's Hit Papers
Peers
Comparison fields: 5 of 93
- Health Informatics 80
- Artificial Intelligence 596
- Radiology, Nuclear Medicine and Imaging 377
- Biophysics 86
- Computer Vision and Pattern Recognition 205
Countries citing papers authored by Thomas de Bel
This map shows the geographic impact of Thomas de Bel'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 Thomas de Bel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas de Bel more than expected).
Fields of papers citing papers by Thomas de Bel
This network shows the impact of papers produced by Thomas de Bel. 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 Thomas de Bel. The network helps show where Thomas de Bel may publish in the future.
Co-authors
The 25 scholars most cited alongside Thomas de Bel, 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 | Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study Hit paper breakdown → | 2020 | 440 |
| 2 | 2019 | 240 | |
| 3 | Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study | 2020 | 78 |
| 4 | 2021 | 63 | |
| 5 | 2020 | 54 | |
| 6 | Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology | 2018 | 48 |
| 7 | 2018 | 37 | |
| 8 | 2021 | 25 | |
| 9 | 2022 | 23 | |
| 10 | 2017 | 20 | |
| 11 | 2019 | 11 | |
| 12 | 2023 | 9 | |
| 13 | 2022 | 9 | |
| 14 | 2022 | 4 | |
| 15 | Deep Learning-Based Histopathologic Assessment of Kidney Tissue | 2019 | 3 |
| 16 | 2023 | 1 | |
| 17 | 2022 | 1 |
About Thomas de Bel
Thomas de Bel is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Oncology and Pulmonary and Respiratory Medicine, having authored 17 papers that have together received 1.1k indexed citations. Recurring topics across this work include AI in cancer detection (11 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Colorectal Cancer Screening and Detection (3 papers), Medical Image Segmentation Techniques (2 papers), Prostate Cancer Diagnosis and Treatment (2 papers), Advanced X-ray and CT Imaging (1 paper) and Magnesium in Health and Disease (1 paper). The work is most often cited by research in Health Informatics (80 citations), Artificial Intelligence (596 citations), Radiology, Nuclear Medicine and Imaging (377 citations), Biophysics (86 citations) and Computer Vision and Pattern Recognition (205 citations). Thomas de Bel has collaborated with scholars based in Netherlands, Sweden and United States. Frequent co-authors include Jeroen van der Laak, Geert Litjens, Hester van Boven, Robert Vink, Bram van Ginneken, Wouter Bulten, Hans Pinckaers, Christina Hulsbergen‐van de Kaa, Meyke Hermsen and Jesper Kers. Their work appears in journals such as The FASEB Journal, Clinical Journal of the American Society of Nephrology, Medical Image Analysis, Pediatric and Developmental Pathology and Cancer Research.
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.