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.
Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) group
2000674 citationsD. Timmerman, L. Valentin et al.Ultrasound in Obstetrics and Gynecologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Herman Verrelst
Since
Specialization
Citations
This map shows the geographic impact of Herman Verrelst'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 Herman Verrelst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Herman Verrelst more than expected).
This network shows the impact of papers produced by Herman Verrelst. 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 Herman Verrelst. The network helps show where Herman Verrelst may publish in the future.
Co-authorship network of co-authors of Herman Verrelst
This figure shows the co-authorship network connecting the top 25 collaborators of Herman Verrelst.
A scholar is included among the top collaborators of Herman Verrelst 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 Herman Verrelst. Herman Verrelst is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Antal, Péter, et al.. (2000). Incorporation of Prior Knowledge in Black-box Models : Comparison of Transformation Methods from Bayesian Network to Multilayer Perceptrons. Uncertainty in Artificial Intelligence. 7–12.2 indexed citations
4.
Timmerman, D., Herman Verrelst, C. Van Holsbeke, et al.. (2000). Prospective evaluation of a new risk of malignancy index. Gynecologic Oncology. 76(2). 255–255.1 indexed citations
5.
Timmerman, D., L. Valentin, T. Bourne, et al.. (2000). Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) group. Ultrasound in Obstetrics and Gynecology. 16(5). 500–505.674 indexed citations breakdown →
6.
Moreau, Yves, et al.. (1999). A hybrid system for fraud detection in mobile communications.. The European Symposium on Artificial Neural Networks. 447–454.15 indexed citations
7.
Moreau, Yves, et al.. (1999). Detection and management of fraud in UMTS networks. Knowledge Discovery and Data Mining. 127–148.2 indexed citations
Vergote, Ignace, et al.. (1999). Univariate and multivariate regression analysis:some basic statistical principles. 262–270.
11.
Verrelst, Herman, Johan A. K. Suykens, Joos Vandewalle, & Bart De Moor. (1998). Bayesian Learning and the Fokker-Planck machine. 55–61.1 indexed citations
12.
Suykens, Johan A. K., Herman Verrelst, & Joos Vandewalle. (1998). On-Line Learning Fokker-Planck Machine. Neural Processing Letters. 7(2). 81–89.8 indexed citations
Verrelst, Herman, Yves Moreau, Joos Vandewalle, & D. Timmerman. (1997). Use of a Multi-Layer Perceptron to Predict Malignancy in Ovarian Tumors. Neural Information Processing Systems. 10. 978–984.4 indexed citations
15.
Moreau, Yves, Herman Verrelst, & Joos Vandewalle. (1997). Fraud detection in mobile communications using supervised neural networks. 149–152.
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.