Arnaud A. A. Setio

17.7k total citations · 2 hit papers
20 papers, 10.3k citations indexed

About

Arnaud A. A. Setio is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Arnaud A. A. Setio has authored 20 papers receiving a total of 10.3k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Pulmonary and Respiratory Medicine and 4 papers in Biomedical Engineering. Recurrent topics in Arnaud A. A. Setio's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Lung Cancer Diagnosis and Treatment (10 papers) and COVID-19 diagnosis using AI (7 papers). Arnaud A. A. Setio is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Lung Cancer Diagnosis and Treatment (10 papers) and COVID-19 diagnosis using AI (7 papers). Arnaud A. A. Setio collaborates with scholars based in Netherlands, Germany and United States. Arnaud A. A. Setio's co-authors include Bram van Ginneken, Francesco Ciompi, Geert Litjens, Clara I. Sá‎nchez, Jeroen van der Laak, Mohsen Ghafoorian, Thijs Kooi, Babak Ehteshami Bejnordi, Colin Jacobs and Paul K. Gerke and has published in prestigious journals such as Radiology, IEEE Transactions on Medical Imaging and Physics in Medicine and Biology.

In The Last Decade

Arnaud A. A. Setio

20 papers receiving 10.0k 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.

A survey on deep learning in medical image analysis

2017 8.3k citations Geert Litjens, Thijs Kooi et al. Medical Image Analysis profile →
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

2016 863 citations Arnaud A. A. Setio, Francesco Ciompi et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Arnaud A. A. Setio Netherlands	15	5.4k	4.4k	2.9k	1.7k	1.5k	20	10.3k
Thijs Kooi Netherlands	5	4.6k 0.9×	4.4k 1.0×	2.6k 0.9×	992 0.6×	1.4k 0.9×	10	9.2k
Mohsen Ghafoorian Netherlands	14	4.4k 0.8×	3.9k 0.9×	2.6k 0.9×	1.0k 0.6×	1.4k 0.9×	27	9.3k
Babak Ehteshami Bejnordi Netherlands	15	4.7k 0.9×	4.8k 1.1×	3.0k 1.0×	928 0.5×	1.4k 0.9×	23	9.6k
Holger R. Roth United States	34	4.5k 0.8×	3.8k 0.9×	3.6k 1.2×	1.2k 0.7×	1.4k 0.9×	105	9.8k
Clara I. Sá‎nchez Netherlands	36	8.3k 1.5×	5.6k 1.3×	3.9k 1.4×	1.8k 1.1×	1.9k 1.2×	108	14.3k
Francesco Ciompi Netherlands	28	6.6k 1.2×	5.7k 1.3×	3.4k 1.2×	2.1k 1.2×	1.8k 1.2×	103	12.6k
Le Lü United States	39	4.0k 0.8×	3.3k 0.8×	3.0k 1.0×	1.2k 0.7×	1.5k 1.0×	188	9.8k
Qi Dou Hong Kong	42	4.5k 0.8×	4.4k 1.0×	4.4k 1.5×	948 0.6×	1.9k 1.2×	174	10.5k
Jianhua Yao United States	50	4.5k 0.8×	3.3k 0.8×	2.6k 0.9×	1.5k 0.9×	2.1k 1.4×	377	12.7k
Jing Qin China	59	4.1k 0.8×	4.5k 1.0×	5.2k 1.8×	1.3k 0.8×	2.1k 1.3×	527	15.6k

Countries citing papers authored by Arnaud A. A. Setio

Since Specialization

Citations

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

Fields of papers citing papers by Arnaud A. A. Setio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arnaud A. A. Setio. 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 Arnaud A. A. Setio. The network helps show where Arnaud A. A. Setio may publish in the future.

Co-authorship network of co-authors of Arnaud A. A. Setio

This figure shows the co-authorship network connecting the top 25 collaborators of Arnaud A. A. Setio. A scholar is included among the top collaborators of Arnaud A. A. Setio 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 Arnaud A. A. Setio. Arnaud A. A. Setio 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

Setio, Arnaud A. A., et al.. (2023). Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis. Frontiers in Public Health. 11. 1239231–1239231. 8 indexed citations

Jacobs, Colin, Arnaud A. A. Setio, Ernst T. Scholten, et al.. (2021). Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists. Radiology Artificial Intelligence. 3(6). e210027–e210027. 29 indexed citations

Setio, Arnaud A. A., Anton Schreuder, Ernst T. Scholten, et al.. (2021). Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT. Radiology. 300(2). 438–447. 98 indexed citations

Yevtushenko, Pavlo, Leonid Goubergrits, Arnaud A. A. Setio, et al.. (2021). Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modeling of Hemodynamics. IEEE Journal of Biomedical and Health Informatics. 26(4). 1815–1825. 21 indexed citations

Thamsen, Bente, Pavlo Yevtushenko, Arnaud A. A. Setio, et al.. (2021). Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability. IEEE Transactions on Medical Imaging. 40(5). 1438–1449. 22 indexed citations

Liu, Siqi, Arnaud A. A. Setio, Florin C. Ghesu, et al.. (2020). No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks. IEEE Transactions on Medical Imaging. 40(1). 335–345. 36 indexed citations

Yang, Jie, Siqi Liu, Saša Grbić, et al.. (2019). Class-Aware Adversarial Lung Nodule Synthesis In CT Images. 1348–1352. 20 indexed citations

Mamani, Gabriel Efrain Humpire, Arnaud A. A. Setio, Bram van Ginneken, & Colin Jacobs. (2018). Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans. Physics in Medicine and Biology. 63(8). 85003–85003. 27 indexed citations

Litjens, Geert, Thijs Kooi, Babak Ehteshami Bejnordi, et al.. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis. 42. 60–88. 8341 indexed citations breakdown →

10.

Mamani, Gabriel Efrain Humpire, Arnaud A. A. Setio, Bram van Ginneken, & Colin Jacobs. (2017). Organ detection in thorax abdomen CT using multi-label convolutional neural networks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 1013416–1013416. 13 indexed citations

11.

Charbonnier, Jean‐Paul, Eva M. van Rikxoort, Arnaud A. A. Setio, et al.. (2016). Improving airway segmentation in computed tomography using leak detection with convolutional networks. Medical Image Analysis. 36. 52–60. 78 indexed citations

12.

Leßmann, Nikolas, Ivana Išgum, Arnaud A. A. Setio, et al.. (2016). Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9785. 978511–978511. 33 indexed citations

13.

Dalmış, Mehmet Ufuk, Geert Litjens, Katharina Holland, et al.. (2016). Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Medical Physics. 44(2). 533–546. 160 indexed citations

14.

Setio, Arnaud A. A., Francesco Ciompi, Geert Litjens, et al.. (2016). Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks. IEEE Transactions on Medical Imaging. 35(5). 1160–1169. 863 indexed citations breakdown →

15.

Setio, Arnaud A. A., et al.. (2015). Automatic detection of large pulmonary solid nodules in thoracic CT images. Medical Physics. 42(10). 5642–5653. 102 indexed citations

16.

Ginneken, Bram van, Arnaud A. A. Setio, Colin Jacobs, & Francesco Ciompi. (2015). Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. 286–289. 197 indexed citations

17.

Setio, Arnaud A. A., Colin Jacobs, Francesco Ciompi, et al.. (2015). Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9414. 94141O–94141O. 3 indexed citations

18.

Setio, Arnaud A. A., Fons van der Sommen, Svitlana Zinger, Erik J. Schoon, & Peter H. N. de With. (2013). Evaluation and Comparison of Textural Feature Representation for the Detection of Early Stage Cancer in Endoscopy. TU/e Research Portal. 238–243. 9 indexed citations

19.

Peemen, Maurice, Arnaud A. A. Setio, Bart Mesman, & Henk Corporaal. (2013). Memory-centric accelerator design for Convolutional Neural Networks. TU/e Research Portal. 13–19. 245 indexed citations

20.

Purwita, Ardimas Andi, Arnaud A. A. Setio, & Trio Adiono. (2011). Optimized 8-level turbo encoder algorithm and VLSI architecture for LTE. 1–6. 3 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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