Thomas de Bel
- 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
- Jeroen van der LaakGeert LitjensRobert VinkWouter BultenBram van GinnekenHester van BovenHans PinckaersChristina Hulsbergen‐van de Kaa
- Topics
- AI in cancer detection (12 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)Colorectal Cancer Screening and Detection (4 papers)
- Partner nations
- NetherlandsSwedenUnited States
In The Last Decade
Thomas de Bel
16 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 641
- Radiology, Nuclear Medicine and Imaging 459
- Pulmonary and Respiratory Medicine 245
- Computer Vision and Pattern Recognition 231
- Oncology 159
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-authorship network of co-authors of Thomas de Bel
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas de Bel. A scholar is included among the top collaborators of Thomas de Bel 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 Thomas de Bel. Thomas de Bel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 1 | |
| 3 | 8 | |
| 4 | 1 | |
| 5 | 21 | |
| 6 | 4 | |
| 7 | 59 | |
| 8 | 24 | |
| 9 | Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study | 77 |
| 10 | Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic studybreakdown → | 421 |
| 11 | 53 | |
| 12 | Deep Learning-Based Histopathologic Assessment of Kidney Tissue | 3 |
| 13 | 10 | |
| 14 | 235 | |
| 15 | Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology | 47 |
| 16 | 36 |
About Thomas de Bel
Thomas de Bel is a scholar working on Health Informatics, Complementary and Manual Therapy and Artificial Intelligence, having authored 16 papers that have together received 1.0k indexed citations. Recurring topics across this work include AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Colorectal Cancer Screening and Detection (4 papers). The work is most often cited by research in Health Informatics (103 citations), Artificial Intelligence (641 citations) and Radiology, Nuclear Medicine and Imaging (459 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, Robert Vink, Wouter Bulten, Bram van Ginneken, Hester van Boven, Hans Pinckaers, Christina Hulsbergen‐van de Kaa, Meyke Hermsen and Jesper Kers. Their work appears in journals such as Cancer Research, Scientific Reports and The Lancet Oncology.
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.