John A. Lee

13.5k total citations · 3 hit papers
281 papers, 9.3k citations indexed

About

John A. Lee is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Computer Vision and Pattern Recognition. According to data from OpenAlex, John A. Lee has authored 281 papers receiving a total of 9.3k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Radiology, Nuclear Medicine and Imaging, 73 papers in Radiation and 59 papers in Computer Vision and Pattern Recognition. Recurrent topics in John A. Lee's work include Advanced Radiotherapy Techniques (71 papers), Medical Imaging Techniques and Applications (62 papers) and Radiation Therapy and Dosimetry (39 papers). John A. Lee is often cited by papers focused on Advanced Radiotherapy Techniques (71 papers), Medical Imaging Techniques and Applications (62 papers) and Radiation Therapy and Dosimetry (39 papers). John A. Lee collaborates with scholars based in Belgium, United Kingdom and United States. John A. Lee's co-authors include Michel Verleysen, Vincent Grégoire, Xavier Geets, Jonathan Van Blerkom, Patrick Davis, Edmond Sterpin, Gareth K. Phoenix, Anne Bol, Pierre Castadot and Terry V. Callaghan and has published in prestigious journals such as Nature, New England Journal of Medicine and The Lancet.

In The Last Decade

John A. Lee

259 papers receiving 8.9k citations

Hit Papers

Nonlinear Dimensionality ... 1995 2026 2005 2015 2007 1995 2021 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
John A. Lee 2.3k 1.8k 1.6k 1.5k 1.4k 281 9.3k
Taesung Park 2.2k 1.0× 215 0.1× 10.6k 6.5× 1.2k 0.8× 3.8k 2.8× 425 24.8k
Le Lü 4.0k 1.7× 152 0.1× 3.0k 1.9× 1.2k 0.8× 3.3k 2.4× 188 9.8k
Jianming Liang 4.9k 2.1× 151 0.1× 5.5k 3.4× 1.0k 0.7× 4.3k 3.1× 89 12.8k
Michael Flynn 2.9k 1.3× 295 0.2× 504 0.3× 1.9k 1.2× 902 0.7× 478 14.8k
Ming‐Hui Chen 544 0.2× 761 0.4× 133 0.1× 5.3k 3.5× 1.5k 1.1× 530 13.7k
Yoshinobu Sato 1.5k 0.6× 146 0.1× 1.6k 1.0× 645 0.4× 474 0.3× 454 7.9k
Stefan Lang 388 0.2× 575 0.3× 491 0.3× 226 0.2× 1.1k 0.8× 257 9.4k
L. E. Peterson 1.1k 0.5× 241 0.1× 266 0.2× 1.4k 0.9× 593 0.4× 224 10.5k
Connor Shorten 1.7k 0.7× 66 0.0× 2.5k 1.5× 303 0.2× 2.9k 2.1× 10 9.0k
Heinz‐Otto Peitgen 1.6k 0.7× 174 0.1× 1.7k 1.0× 784 0.5× 1.2k 0.9× 207 8.4k

Countries citing papers authored by John A. Lee

Since Specialization
Citations

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

Fields of papers citing papers by John A. Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John A. Lee

This figure shows the co-authorship network connecting the top 25 collaborators of John A. Lee. A scholar is included among the top collaborators of John A. Lee 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 John A. Lee. John A. Lee 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.
Lee, John A., et al.. (2025). Anticipating potential bottlenecks in adaptive proton FLASH therapy: a ridge filter reuse strategy. Physics in Medicine and Biology. 70(6). 65005–65005.
2.
Wuyckens, S., et al.. (2025). FLASH-enabled proton SBRT for a challenging case of spine metastasis. Physics in Medicine and Biology. 70(19). 19NT01–19NT01.
3.
Nguyen, Dan, et al.. (2024). Can input reconstruction be used to directly estimate uncertainty of a dose prediction U‐Net model?. Medical Physics. 51(10). 7369–7377. 1 indexed citations
4.
Verleysen, Michel, et al.. (2024). Investigating latent representations and generalization in deep neural networks for tabular data. Neurocomputing. 597. 127967–127967. 1 indexed citations
5.
Wuyckens, S., et al.. (2024). The proton arc therapy treatment planning problem is NP-Hard. Computers in Biology and Medicine. 171. 108139–108139. 2 indexed citations
6.
Lee, John A., et al.. (2024). DIVE-ART: A tool to guide clinicians towards dosimetrically informed volume editions of automatically segmented volumes in adaptive radiation therapy. Radiotherapy and Oncology. 192. 110108–110108. 6 indexed citations
8.
Souris, Kevin, et al.. (2023). A spot‐specific range uncertainty framework for robust optimization of proton therapy treatments. Medical Physics. 50(10). 6554–6568. 1 indexed citations
9.
Wuyckens, S., Lewei Zhao, Guillaume Janssens, et al.. (2022). Bi-criteria Pareto optimization to balance irradiation time and dosimetric objectives in proton arc therapy. Physics in Medicine and Biology. 67(24). 245017–245017. 7 indexed citations
10.
Montero, Ana María Barragán, Mélissa Thomas, Gilles Defraene, et al.. (2022). Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer. Radiotherapy and Oncology. 176. 101–107. 18 indexed citations
11.
Montero, Ana María Barragán, Mélissa Thomas, Gilles Defraene, et al.. (2021). Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance. Physica Medica. 83. 52–63. 46 indexed citations
12.
Léger, Jean Michel, et al.. (2020). Cross-Domain Data Augmentation for Deep-Learning-Based Male Pelvic Organ Segmentation in Cone Beam CT. Applied Sciences. 10(3). 1154–1154. 16 indexed citations
13.
Verleysen, Michel, et al.. (2020). Fast Multiscale Neighbor Embedding. IEEE Transactions on Neural Networks and Learning Systems. 33(4). 1546–1560. 10 indexed citations
14.
Sterpin, Edmond, Ana María Barragán Montero, Kevin Souris, & John A. Lee. (2016). Planification de traitement robuste en protonthérapie. Cancer/Radiothérapie. 20(6-7). 523–529. 1 indexed citations
15.
Lee, John A., Diego H. Peluffo-Ordóńez, & Michel Verleysen. (2015). Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure. Neurocomputing. 169. 246–261. 51 indexed citations
16.
Sterpin, Edmond, et al.. (2014). Validation of the mid-position strategy for lung tumors in helical TomoTherapy. Radiotherapy and Oncology. 110(3). 529–537. 32 indexed citations
17.
Hong, Sun-Kee & John A. Lee. (2006). Global environmental changes in terrestrial ecosystems. International issues and strategic solutions: introduction. Ecological Research. 21(6). 783–787. 14 indexed citations
18.
Bishop, Ann Peterson, D Arthur, Carolina Are, et al.. (1967). PAR volume 57 issue 2 Cover and Front matter. Parasitology. 57(2). f1–f9. 1 indexed citations
19.
Bishop, Ann Peterson, D Arthur, Carolina Are, et al.. (1967). PAR volume 57 issue 3 Cover and Front matter. Parasitology. 57(3). f1–f9. 1 indexed citations
20.
Bishop, Ann Peterson, D Arthur, Carolina Are, et al.. (1967). PAR volume 57 issue 4 Cover and Front matter. Parasitology. 57(4). f1–f9. 1 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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