Jan Luts

1.2k total citations
20 papers, 813 citations indexed

About

Jan Luts is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Jan Luts has authored 20 papers receiving a total of 813 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Molecular Biology and 5 papers in Artificial Intelligence. Recurrent topics in Jan Luts's work include Advanced MRI Techniques and Applications (7 papers), Brain Tumor Detection and Classification (4 papers) and Gene expression and cancer classification (4 papers). Jan Luts is often cited by papers focused on Advanced MRI Techniques and Applications (7 papers), Brain Tumor Detection and Classification (4 papers) and Gene expression and cancer classification (4 papers). Jan Luts collaborates with scholars based in Belgium, Spain and Netherlands. Jan Luts's co-authors include Sabine Van Huffel, Johan A. K. Suykens, Bart De Moor, Raf Van de Plas, Fabian Ojeda, Arend Heerschap, L.M.C. Buydens, Geert Postma, Hans Wouters and Hadassa Degani and has published in prestigious journals such as Magnetic Resonance in Medicine, Analytica Chimica Acta and Fertility and Sterility.

In The Last Decade

Jan Luts

19 papers receiving 780 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan Luts Belgium 11 251 140 108 106 101 20 813
Ling Wei China 24 399 1.6× 574 4.1× 168 1.6× 59 0.6× 122 1.2× 95 1.6k
Yusuke Fujita Japan 17 178 0.7× 120 0.9× 240 2.2× 36 0.3× 139 1.4× 57 1.2k
Wei Kong China 18 738 2.9× 63 0.5× 69 0.6× 66 0.6× 166 1.6× 105 1.4k
Zhenye Li China 18 232 0.9× 202 1.4× 42 0.4× 90 0.8× 116 1.1× 88 1.5k
Arthur Tenenhaus France 16 321 1.3× 182 1.3× 35 0.3× 57 0.5× 61 0.6× 26 988
Steven Wang United States 16 342 1.4× 85 0.6× 211 2.0× 16 0.2× 71 0.7× 40 1.0k
Limin Yang United States 16 380 1.5× 79 0.6× 123 1.1× 65 0.6× 65 0.6× 60 828
Lewis E. Lipkin United States 20 577 2.3× 62 0.4× 26 0.2× 28 0.3× 46 0.5× 52 1.2k
Weibin Chen China 17 204 0.8× 79 0.6× 51 0.5× 12 0.1× 23 0.2× 61 869

Countries citing papers authored by Jan Luts

Since Specialization
Citations

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

Fields of papers citing papers by Jan Luts

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jan Luts. 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 Jan Luts. The network helps show where Jan Luts may publish in the future.

Co-authorship network of co-authors of Jan Luts

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

All Works

20 of 20 papers shown
1.
Luts, Jan, et al.. (2018). Semiparametric Regression Analysis via Infer.NET. Journal of Statistical Software. 87(2). 6 indexed citations
2.
Luts, Jan. (2014). Real-Time Semiparametric Regression for Distributed Data Sets. IEEE Transactions on Knowledge and Data Engineering. 27(2). 545–557. 6 indexed citations
3.
Luts, Jan & John T. Ormerod. (2013). Mean field variational Bayesian inference for support vector machine classification. Computational Statistics & Data Analysis. 73. 163–176. 10 indexed citations
4.
Cauter, Sofie Van, Diana M. Sima, Jan Luts, et al.. (2012). Reproducibility of rapid short echo time CSI at 3 tesla for clinical applications. Journal of Magnetic Resonance Imaging. 37(2). 445–456. 12 indexed citations
5.
Postma, Geert, Jan Luts, Albert J. S. Idema, et al.. (2011). On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation. Computers in Biology and Medicine. 41(2). 87–97. 9 indexed citations
6.
Sáez, Carlos, Juan M. García‐Gómez, Salvador Tortajada, et al.. (2011). A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents. The Knowledge Engineering Review. 26(3). 283–301. 8 indexed citations
7.
Luts, Jan, et al.. (2011). Asymptotic hypothesis test to compare likelihood ratios of multiple diagnostic tests in unpaired designs. Journal of Statistical Planning and Inference. 141(11). 3578–3594. 4 indexed citations
8.
Luts, Jan, Geert Molenberghs, Geert Verbeke, Sabine Van Huffel, & Johan A. K. Suykens. (2011). A mixed effects least squares support vector machine model for classification of longitudinal data. Computational Statistics & Data Analysis. 56(3). 611–628. 25 indexed citations
9.
10.
Luts, Jan, Fabian Ojeda, Raf Van de Plas, et al.. (2010). A tutorial on support vector machine-based methods for classification problems in chemometrics. Analytica Chimica Acta. 665(2). 129–145. 242 indexed citations
11.
Werbrouck, E., Jan Luts, Sabine Van Huffel, et al.. (2010). Detection of endometrial pathology using saline infusion sonography versus gel instillation sonography: a prospective cohort study. Fertility and Sterility. 95(1). 285–288. 23 indexed citations
12.
Podo, Franca, L.M.C. Buydens, Hadassa Degani, et al.. (2010). Triple‐negative breast cancer: Present challenges and new perspectives. Molecular Oncology. 4(3). 209–229. 248 indexed citations
13.
Luts, Jan. (2010). Classification of Brain Tumors Based on Magnetic Resonance Spectroscopy (Classificatie van hersentumoren op basis van magnetische resonantie spectroscopie). 1 indexed citations
14.
Luts, Jan, Johan A. K. Suykens, Sabine Van Huffel, et al.. (2009). Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI. 27. 1–8. 7 indexed citations
15.
Luts, Jan, Teresa Laudadio, M. Carmen Martínez‐Bisbal, Bernardo Celda, & Sabine Van Huffel. (2009). Differentation between brain metastasis and glioblastoma using MRI and two-dimensional Turbo spectroscopic imaging data.
16.
Luts, Jan, Jean‐Baptiste Poullet, Juan M. García‐Gómez, et al.. (2008). Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra. Magnetic Resonance in Medicine. 60(2). 288–298. 22 indexed citations
17.
García‐Gómez, Juan M., Salvador Tortajada, Margarida Julià‐Sapé, et al.. (2008). The effect of combining two echo times in automatic brain tumor classification by MRS. NMR in Biomedicine. 21(10). 1112–1125. 32 indexed citations
18.
Luts, Jan, Teresa Laudadio, Albert J. S. Idema, et al.. (2008). Nosologic imaging of the brain: segmentation and classification using MRI and MRSI. NMR in Biomedicine. 22(4). 374–390. 47 indexed citations
19.
Calster, Ben Van, Jan Luts, Johan A. K. Suykens, et al.. (2007). Comparing methods for multi-class probabilities in medical decision making using LS-SVMs and kernel logistic regression. Lecture notes in computer science. 4669. 139–148. 1 indexed citations
20.
Luts, Jan, Arend Heerschap, Johan A. K. Suykens, & Sabine Van Huffel. (2007). A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. Artificial Intelligence in Medicine. 40(2). 87–102. 71 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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