Thomas de Bel

2.8k total citations · 1 hit paper
16 papers, 1.0k citations indexed

About

Thomas de Bel is a scholar working on Artificial Intelligence, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Thomas de Bel has authored 16 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 6 papers in Oncology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Thomas de Bel's 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). Thomas de Bel is often cited by papers focused on AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Colorectal Cancer Screening and Detection (4 papers). Thomas de Bel collaborates with scholars based in Netherlands, Sweden and United States. Thomas de Bel's 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 and has published in prestigious journals such as Cancer Research, Scientific Reports and The Lancet Oncology.

In The Last Decade

Thomas de Bel

16 papers receiving 1.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

2020 421 citations Wouter Bulten, Hans Pinckaers et al. The Lancet Oncology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Thomas de Bel Netherlands	10	641	459	245	231	159	16	1.0k
Hans Pinckaers Netherlands	12	608 0.9×	477 1.0×	217 0.9×	183 0.8×	150 0.9×	20	899
Wouter Bulten Netherlands	8	752 1.2×	530 1.2×	210 0.9×	275 1.2×	157 1.0×	9	998
Lily H. Peng United States	6	446 0.7×	376 0.8×	134 0.5×	96 0.4×	93 0.6×	6	689
Alexi Baidoshvili Netherlands	15	492 0.8×	346 0.8×	89 0.4×	193 0.8×	149 0.9×	22	920
Luke Geneslaw United States	4	1.1k 1.7×	773 1.7×	164 0.7×	401 1.7×	340 2.1×	4	1.5k
Norman Zerbe Germany	12	501 0.8×	314 0.7×	88 0.4×	194 0.8×	118 0.7×	34	780
Fahdi Kanavati Japan	11	500 0.8×	504 1.1×	168 0.7×	122 0.5×	218 1.4×	20	794
Jeremias Krause Germany	3	466 0.7×	489 1.1×	106 0.4×	99 0.4×	305 1.9×	8	868
Anurag Vaidya United States	8	480 0.7×	407 0.9×	83 0.3×	117 0.5×	104 0.7×	10	865
Iringo Kovacs Netherlands	5	538 0.8×	386 0.8×	109 0.4×	181 0.8×	105 0.7×	7	762

Countries citing papers authored by Thomas de Bel

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Bel, Thomas de, et al.. (2023). Automated Deep Learning-Based Classification of Wilms Tumor Histopathology. Cancers. 15(9). 2656–2656. 9 indexed citations

Bel, Thomas de, Tanya L. Hoskin, Stacey J. Winham, et al.. (2023). Abstract 3587: High resolution microCT to analyze the 3D morphology of microcalcifications in benign breast disease and breast cancer biopsy tissues. Cancer Research. 83(7_Supplement). 3587–3587. 1 indexed citations

Bel, Thomas de, Geert Litjens, Joshua Ogony, et al.. (2022). Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning. npj Breast Cancer. 8(1). 13–13. 8 indexed citations

Sherman, Mark E., Thomas de Bel, Michael G. Heckman, et al.. (2022). Serum hormone levels and normal breast histology among premenopausal women. Breast Cancer Research and Treatment. 194(1). 149–158. 1 indexed citations

Kurstjens, Steef, et al.. (2022). Automated prediction of low ferritin concentrations using a machine learning algorithm. Clinical Chemistry and Laboratory Medicine (CCLM). 60(12). 1921–1928. 21 indexed citations

Bel, Thomas de, et al.. (2022). Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?. Pediatric and Developmental Pathology. 25(4). 380–387. 4 indexed citations

Bel, Thomas de, John‐Melle Bokhorst, Jeroen van der Laak, & Geert Litjens. (2021). Residual cyclegan for robust domain transformation of histopathological tissue slides. Medical Image Analysis. 70. 102004–102004. 59 indexed citations

Balkenhol, Maschenka, Francesco Ciompi, Żaneta Świderska-Chadaj, et al.. (2021). Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics. The Breast. 56. 78–87. 24 indexed citations

Bulten, Wouter, Hans Pinckaers, Hester van Boven, et al.. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. arXiv (Cornell University). 77 indexed citations

10.

Bulten, Wouter, Hans Pinckaers, Hester van Boven, et al.. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology. 21(2). 233–241. 421 indexed citations breakdown →

11.

Świderska-Chadaj, Żaneta, Thomas de Bel, Lionel Blanchet, et al.. (2020). Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer. Scientific Reports. 10(1). 53 indexed citations

12.

Hermsen, Meyke, Thomas de Bel, Eric J. Steenbergen, et al.. (2019). Deep Learning-Based Histopathologic Assessment of Kidney Tissue. Clinical Journal of the American Society of Nephrology. 30(10). 1968–1979. 3 indexed citations

13.

Kurstjens, Steef, Bart Smeets, Caro Bos, et al.. (2019). Renal phospholipidosis and impaired magnesium handling in high‐fat‐diet–fed mice. The FASEB Journal. 33(6). 7192–7201. 10 indexed citations

14.

Hermsen, Meyke, Thomas de Bel, Eric J. Steenbergen, et al.. (2019). Deep Learning–Based Histopathologic Assessment of Kidney Tissue. Journal of the American Society of Nephrology. 30(10). 1968–1979. 235 indexed citations

15.

Bel, Thomas de, Meyke Hermsen, Jesper Kers, Jeroen van der Laak, & Geert Litjens. (2018). Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology. Pure Amsterdam UMC. 151–163. 47 indexed citations

16.

Bel, Thomas de, Meyke Hermsen, Jeroen van der Laak, et al.. (2018). Automatic segmentation of histopathological slides of renal tissue using deep learning. 37–37. 36 indexed citations

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.

Explore authors with similar magnitude of impact