Thijs Kooi

15.0k total citations · 2 hit papers
10 papers, 9.2k citations indexed

About

Thijs Kooi is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Thijs Kooi has authored 10 papers receiving a total of 9.2k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Thijs Kooi's work include AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Imaging Techniques and Applications (2 papers). Thijs Kooi is often cited by papers focused on AI in cancer detection (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Medical Imaging Techniques and Applications (2 papers). Thijs Kooi collaborates with scholars based in Netherlands and United States. Thijs Kooi's co-authors include Bram van Ginneken, Geert Litjens, Clara I. Sá‎nchez, Arnaud A. A. Setio, Jeroen van der Laak, Babak Ehteshami Bejnordi, Francesco Ciompi, Mohsen Ghafoorian, Nico Karssemeijer and Albert Gubern‐Mérida and has published in prestigious journals such as Medical Physics, Medical Image Analysis and Journal of Medical Imaging.

In The Last Decade

Thijs Kooi

10 papers receiving 8.9k citations

Hit Papers

A survey on deep learning in medical image analysis 2016 2026 2019 2022 2017 2016 2.5k 5.0k 7.5k
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.

  1. A survey on deep learning in medical image analysis
    2017 8.3k citations Geert Litjens, Thijs Kooi et al. Medical Image Analysis profile →
  2. Large scale deep learning for computer aided detection of mammographic lesions
    2016 676 citations Thijs Kooi, Geert Litjens et al. Medical Image Analysis profile →

Peers

Thijs Kooi
Mohsen Ghafoorian Netherlands
Babak Ehteshami Bejnordi Netherlands
Arnaud A. A. Setio Netherlands
Holger R. Roth United States
Le Lü United States
Clara I. Sá‎nchez Netherlands
Qi Dou Hong Kong
Francesco Ciompi Netherlands
Nima Tajbakhsh United States
Jianhua Yao United States
Mohsen Ghafoorian Netherlands
Thijs Kooi
Citations per year, relative to Thijs Kooi Thijs Kooi (= 1×) peers Mohsen Ghafoorian

Countries citing papers authored by Thijs Kooi

Since Specialization
Citations

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

Fields of papers citing papers by Thijs Kooi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thijs Kooi

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

All Works

10 of 10 papers shown
1.
Dalmış, Mehmet Ufuk, Suzan Vreemann, Thijs Kooi, et al.. (2018). Fully automated detection of breast cancer in screening MRI using convolutional neural networks. Journal of Medical Imaging. 5(1). 1–1. 52 indexed citations
2.
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 →
3.
Kooi, Thijs & Nico Karssemeijer. (2017). Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 101341J–101341J. 4 indexed citations
4.
Kooi, Thijs & Nico Karssemeijer. (2017). Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks. Journal of Medical Imaging. 4(4). 1–1. 41 indexed citations
5.
Kooi, Thijs, et al.. (2017). Conditional random field modelling of interactions between findings in mammography. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 101341E–101341E. 1 indexed citations
6.
Kooi, Thijs & Nico Karssemeijer. (2017). Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks. arXiv (Cornell University). 4. 1 indexed citations
7.
Kooi, Thijs, et al.. (2017). Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network. Medical Physics. 44(3). 1017–1027. 77 indexed citations
8.
Kooi, Thijs, Geert Litjens, Bram van Ginneken, et al.. (2016). Large scale deep learning for computer aided detection of mammographic lesions. Medical Image Analysis. 35. 303–312. 676 indexed citations breakdown →
9.
Kooi, Thijs & Nico Karssemeijer. (2014). Boosting classification performance in computer aided diagnosis of breast masses in raw full-field digital mammography using processed and screen film images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9035. 90351B–90351B. 3 indexed citations
10.
Wiering, Marco & Thijs Kooi. (2010). Region enhanced neural Q-learning for solving model-based POMDPs. 27. 1–8. 2 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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