Fons van der Sommen

3.2k total citations · 1 hit paper
130 papers, 1.6k citations indexed

About

Fons van der Sommen is a scholar working on Surgery, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Fons van der Sommen has authored 130 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Surgery, 54 papers in Oncology and 49 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Fons van der Sommen's work include Esophageal Cancer Research and Treatment (50 papers), Colorectal Cancer Screening and Detection (47 papers) and Radiomics and Machine Learning in Medical Imaging (34 papers). Fons van der Sommen is often cited by papers focused on Esophageal Cancer Research and Treatment (50 papers), Colorectal Cancer Screening and Detection (47 papers) and Radiomics and Machine Learning in Medical Imaging (34 papers). Fons van der Sommen collaborates with scholars based in Netherlands, Germany and Belgium. Fons van der Sommen's co-authors include Erik J. Schoon, Peter H. N. de With, Wouter L. Curvers, Jacques Bergman, Svitlana Zinger, Maarten R. Struyvenberg, Joost van der Putten, Raf Bisschops, Sveta Zinger and Peter H. de With and has published in prestigious journals such as SHILAP Revista de lepidopterología, Gastroenterology and Gut.

In The Last Decade

Fons van der Sommen

111 papers receiving 1.6k citations

Hit Papers

align trajectories sort by overperformance

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.

Deep-Learning System Detects Neoplasia in Patients With Barrett’s Esophagus With Higher Accuracy Than Endoscopists in a Multistep Training and Validation Study With Benchmarking

2019 229 citations Albert J. de Groof, Maarten R. Struyvenberg et al. Gastroenterology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fons van der Sommen Netherlands	19	859	806	633	534	220	130	1.6k
Kazuharu Aoyama Japan	16	1.3k 1.5×	949 1.2×	1.2k 2.0×	747 1.4×	331 1.5×	25	2.4k
Satoki Shichijo Japan	23	1.8k 2.0×	1.4k 1.8×	1.1k 1.7×	608 1.1×	280 1.3×	119	2.7k
Xiaogang Liu China	12	647 0.8×	227 0.3×	1.1k 1.7×	726 1.4×	419 1.9×	34	1.6k
Masashi Misawa Japan	30	1.4k 1.7×	859 1.1×	2.2k 3.5×	904 1.7×	457 2.1×	139	2.9k
Keigo Matsuo Japan	9	481 0.6×	394 0.5×	509 0.8×	305 0.6×	148 0.7×	29	992
Yusuke Horiuchi Japan	23	1.2k 1.4×	942 1.2×	564 0.9×	339 0.6×	115 0.5×	91	1.7k
Vincent Agnus France	22	321 0.4×	692 0.9×	327 0.5×	490 0.9×	52 0.2×	62	1.4k
Maria Vakalopoulou France	12	501 0.6×	121 0.2×	432 0.7×	1.1k 2.0×	223 1.0×	30	1.5k
Omer F. Ahmad United Kingdom	13	205 0.2×	173 0.2×	331 0.5×	191 0.4×	106 0.5×	38	658
Tatsuya Ohnishi Japan	8	313 0.4×	189 0.2×	287 0.5×	201 0.4×	110 0.5×	18	652

Countries citing papers authored by Fons van der Sommen

Since Specialization

Citations

This map shows the geographic impact of Fons van der Sommen'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 Fons van der Sommen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fons van der Sommen more than expected).

Fields of papers citing papers by Fons van der Sommen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fons van der Sommen. 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 Fons van der Sommen. The network helps show where Fons van der Sommen may publish in the future.

Co-authorship network of co-authors of Fons van der Sommen

This figure shows the co-authorship network connecting the top 25 collaborators of Fons van der Sommen. A scholar is included among the top collaborators of Fons van der Sommen 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 Fons van der Sommen. Fons van der Sommen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Boers, Tim, Kiki Fockens, Joost van der Putten, et al.. (2025). Comparison of graphic user interfaces for computer-aided detection in Barrett’s neoplasia. Gastrointestinal Endoscopy. 102(5). 662–670.

Fockens, Kiki, Tim Boers, Joost van der Putten, et al.. (2025). Challenges in Implementing Endoscopic Artificial Intelligence: The Impact of Real‐World Imaging Conditions on Barrett's Neoplasia Detection. United European Gastroenterology Journal. 13(6). 929–937. 2 indexed citations

Nederend, Joost, et al.. (2025). Clinical insights to improve medical deep learning design: A comprehensive review of methods and benefits. Computers in Biology and Medicine. 196(Pt C). 110780–110780. 1 indexed citations

Nederend, Joost, et al.. (2025). Identifying key challenges in ovarian tumor classification: a comparative study using deep learning and radiomics. TU/e Research Portal. 69–69.

Groof, Albert J. de, et al.. (2025). Self-supervised learning for automated image quality assessment in endoscopy. Pure Amsterdam UMC. 46–46.

Duits, Lucas C., Roos E. Pouw, Bas L. Weusten, et al.. (2025). Evaluation of an improved computer-aided detection system for Barrett’s neoplasia in real-world imaging conditions. Endoscopy. 57(12). 1327–1337.

Fockens, Kiki, Britt B. S. L. Houwen, Manon van der Vlugt, et al.. (2025). Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems. Endoscopy. 57(6). 602–610. 2 indexed citations

Piek, Jurgen M.J., Fons van der Sommen, Max J. Lahaye, et al.. (2025). Artificial Intelligence and radiomics models for the diagnosis and prognosis of peritoneal metastases on imaging: a systematic review and meta-analysis. Computers in Biology and Medicine. 198(Pt B). 111188–111188.

Masclee, Ad, et al.. (2024). Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction. Gastrointestinal Endoscopy. 100(6). 1070–1078.e10. 9 indexed citations

10.

Jacobs, Igor, et al.. (2024). Robustness evaluation of CAD systems for lung nodule segmentation using clinically relevant image perturbations. TU/e Research Portal. 10. 76–76. 1 indexed citations

11.

Schor, Marieke, Peter H. N. de With, Fons van der Sommen, et al.. (2024). Improving the endoscopic recognition of early colorectal carcinoma using artificial intelligence: current evidence and future directions. SHILAP Revista de lepidopterología. 12(10). E1102–E1117.

12.

Fockens, Kiki, et al.. (2024). Tu2011 IMAGE QUALITY CHALLENGES IN AI: IMPROVING ROBUSTNESS OF A COMPUTER AIDED DETECTION SYSTEM FOR BARRETT'S NEOPLASIA.. Gastroenterology. 166(5). S–1491. 2 indexed citations

13.

Kusters, Koen, Kiki Fockens, Tim Boers, et al.. (2024). DOMAIN-SPECIFIC DATA AUGMENTATION FOR ROBUST AI SYSTEMS IN ENDOSCOPY. Gastrointestinal Endoscopy. 99(6). AB27–AB28. 1 indexed citations

14.

Winkens, Björn, et al.. (2024). Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. TU/e Research Portal. 5(1). 2 indexed citations

15.

Boers, Tim, Kiki Fockens, Jeroen de Groof, et al.. (2023). Barrett's lesion detection using a minimal integer-based neural network for embedded systems integration. Pure Amsterdam UMC. 1 indexed citations

16.

Putten, Joost van der, Xiaojuan Zhao, Arend Karrenbeld, et al.. (2023). Optical Biopsy of Dysplasia in Barrett’s Oesophagus Assisted by Artificial Intelligence. Cancers. 15(7). 1950–1950. 3 indexed citations

17.

With, Peter H. N. de, et al.. (2023). Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data. 31–31. 3 indexed citations

18.

Sommen, Fons van der, Himar Fabelo, Svitlana Zinger, et al.. (2020). Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach. Sensors. 20(23). 6955–6955. 49 indexed citations

19.

Arribas, Julia, Giulio Antonelli, Leonardo Frazzoni, et al.. (2020). Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis. Gut. 70(8). 1458–1468. 50 indexed citations

20.

Groof, Jeroen de, Fons van der Sommen, Joost van der Putten, et al.. (2019). The Argos project: The development of a computer‐aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterology Journal. 7(4). 538–547. 92 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.

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