Jens Kleesiek

5.6k total citations · 3 hit papers
107 papers, 2.3k citations indexed

About

Jens Kleesiek is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Jens Kleesiek has authored 107 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Radiology, Nuclear Medicine and Imaging, 36 papers in Artificial Intelligence and 22 papers in Biomedical Engineering. Recurrent topics in Jens Kleesiek's work include Radiomics and Machine Learning in Medical Imaging (37 papers), AI in cancer detection (22 papers) and Artificial Intelligence in Healthcare and Education (19 papers). Jens Kleesiek is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (37 papers), AI in cancer detection (22 papers) and Artificial Intelligence in Healthcare and Education (19 papers). Jens Kleesiek collaborates with scholars based in Germany, Austria and United States. Jens Kleesiek's co-authors include Jan Egger, Martin Bendszus, Gregor Urban, Jianning Li, Klaus Maier‐Hein, Armin Biller, Dániel Schwarz, Alexander Hubert, Behrus Puladi and Christina Gsaxner and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Jens Kleesiek

90 papers receiving 2.3k citations

Hit Papers

3 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.

Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

2016 331 citations Jens Kleesiek, Klaus Maier‐Hein et al. profile →
ChatGPT in healthcare: A taxonomy and systematic review

2024 171 citations Jianning Li, Amin Dada et al. Computer Methods and Programs in Biomedicine profile →
CellViT: Vision Transformers for precise cell segmentation and classification

2024 96 citations Constantin Seibold, Julius Keyl et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jens Kleesiek Germany	21	994	503	501	436	385	107	2.3k
Ahmad Chaddad Canada	27	1.4k 1.4×	715 1.4×	317 0.6×	342 0.8×	443 1.2×	111	2.4k
Ashirbani Saha Canada	24	1.3k 1.3×	890 1.8×	506 1.0×	289 0.7×	230 0.6×	69	2.3k
Daniel Chow United States	28	1.4k 1.5×	527 1.0×	172 0.3×	323 0.7×	447 1.2×	95	2.6k
Panagiotis Korfiatis United States	25	1.7k 1.7×	746 1.5×	313 0.6×	239 0.5×	434 1.1×	67	3.0k
Zeynettin Akkus United States	21	1.5k 1.5×	800 1.6×	579 1.2×	492 1.1×	580 1.5×	54	3.2k
Timothy L. Kline United States	29	1.7k 1.7×	670 1.3×	309 0.6×	200 0.5×	514 1.3×	102	3.5k
Narendra N. Khanna India	28	830 0.8×	440 0.9×	318 0.6×	450 1.0×	888 2.3×	116	2.7k
Ken Chang United States	23	1.4k 1.4×	521 1.0×	169 0.3×	151 0.3×	336 0.9×	51	2.2k
Jun Xia China	25	2.0k 2.0×	1.2k 2.4×	378 0.8×	261 0.6×	464 1.2×	124	4.0k
Evangelia I. Zacharaki Greece	24	1.4k 1.4×	499 1.0×	791 1.6×	615 1.4×	340 0.9×	88	2.8k

Countries citing papers authored by Jens Kleesiek

Since Specialization

Citations

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

Fields of papers citing papers by Jens Kleesiek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jens Kleesiek

This figure shows the co-authorship network connecting the top 25 collaborators of Jens Kleesiek. A scholar is included among the top collaborators of Jens Kleesiek 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 Jens Kleesiek. Jens Kleesiek 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:

20 of 20 papers shown

Gsaxner, Christina, Stephan Lang, Jens Kleesiek, et al.. (2025). Performing the HINTS-exam using a mixed-reality head-mounted display in patients with acute vestibular syndrome: a feasibility study. Frontiers in Neurology. 16. 1576959–1576959.

Nensa, Felix, et al.. (2024). Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis. EBioMedicine. 111. 105526–105526.

Neher, Peter, Maximilian Zenk, Moon Kim, et al.. (2024). Real-world federated learning in radiology: hurdles to overcome and benefits to gain. Journal of the American Medical Informatics Association. 32(1). 193–205. 5 indexed citations

Seibold, Constantin, Julius Keyl, Giulia Baldini, et al.. (2024). CellViT: Vision Transformers for precise cell segmentation and classification. Medical Image Analysis. 94. 103143–103143. 96 indexed citations breakdown →

Li, Jianning, Amin Dada, Behrus Puladi, Jens Kleesiek, & Jan Egger. (2024). ChatGPT in healthcare: A taxonomy and systematic review. Computer Methods and Programs in Biomedicine. 245. 108013–108013. 171 indexed citations breakdown →

Kleesiek, Jens, et al.. (2024). MultiAR: A Multi-User Augmented Reality Platform for Biomedical Education. PubMed. 2024. 1–7.

Wild, Daniel, et al.. (2024). Deep Learning-Based Point Cloud Registration for Augmented Reality-Guided Surgery. Universitätsbibliographie, Universität Duisburg-Essen. 1–5. 1 indexed citations

Jäger, Paul F., et al.. (2024). Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 10998–11018. 8 indexed citations

Han, Tianyu, Sven Nebelung, Firas Khader, et al.. (2024). Medical large language models are susceptible to targeted misinformation attacks. npj Digital Medicine. 7(1). 288–288. 17 indexed citations

10.

Puladi, Behrus, et al.. (2024). Generalisation of Segmentation Using Generative Adversarial Networks. Universitätsbibliographie, Universität Duisburg-Essen. 1–4.

11.

Kleesiek, Jens, et al.. (2024). A Baseline Solution for the ISBI 2024 Dreaming Challenge. Universitätsbibliographie, Universität Duisburg-Essen. 1–3. 1 indexed citations

12.

Ting, Saskia, Sven‐Thorsten Liffers, Kelsey L. Pomykala, et al.. (2023). Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning. JCO Clinical Cancer Informatics. 7(7). e2300038–e2300038. 6 indexed citations

13.

Li, Jianning, A.J.M. Ferreira, Behrus Puladi, et al.. (2023). Open-source skull reconstruction with MONAI. SoftwareX. 23. 101432–101432. 2 indexed citations

14.

Kim, Moon, Robert Seifert, David Kersting, et al.. (2023). Evaluation of thresholding methods for the quantification of [68Ga]Ga-PSMA-11 PET molecular tumor volume and their effect on survival prediction in patients with advanced prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy. European Journal of Nuclear Medicine and Molecular Imaging. 50(7). 2196–2209. 12 indexed citations

15.

Bucher, Andreas, et al.. (2023). Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology. Healthcare. 11(17). 2377–2377.

16.

Sekuboyina, Anjany, et al.. (2022). Beyond Medical Imaging - A Review of Multimodal Deep Learning in Radiology. Zurich Open Repository and Archive (University of Zurich). 4 indexed citations

17.

Keyl, Julius, René Hosch, Simon Bogner, et al.. (2022). Deep learning‐based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer. Journal of Cachexia Sarcopenia and Muscle. 14(1). 545–552. 19 indexed citations

18.

Fink, Matthias A., Constantin Seibold, Rainer Stiefelhagen, et al.. (2021). CT Angiography Clot Burden Score from Data Mining of Structured Reports for Pulmonary Embolism. Radiology. 302(1). 175–184. 12 indexed citations

19.

Seifert, Robert, et al.. (2020). Künstliche Intelligenz in der Hybridbildgebung. Der Radiologe. 60(5). 405–412. 3 indexed citations

20.

Biller, Armin, Stephanie Badde, Armin M. Nagel, et al.. (2015). Improved Brain Tumor Classification by Sodium MR Imaging: Prediction ofIDHMutation Status and Tumor Progression. American Journal of Neuroradiology. 37(1). 66–73. 50 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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