Paul Desbordes

869 total citations · 1 hit paper
13 papers, 534 citations indexed

About

Paul Desbordes is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Paul Desbordes has authored 13 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Pulmonary and Respiratory Medicine, 6 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Artificial Intelligence. Recurrent topics in Paul Desbordes's work include Medical Imaging Techniques and Applications (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (3 papers). Paul Desbordes is often cited by papers focused on Medical Imaging Techniques and Applications (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (3 papers). Paul Desbordes collaborates with scholars based in France, Belgium and United States. Paul Desbordes's co-authors include Isabelle Gardin, Su Ruan, John A. Lee, Romain Modzelewski, Liesbeth Vandewinckele, Kevin Souris, Gilmer Valdés, Benoît Macq, Siri Willems and Steven Michiels and has published in prestigious journals such as PLoS ONE, IEEE Access and European Journal of Nuclear Medicine and Molecular Imaging.

In The Last Decade

Paul Desbordes

10 papers receiving 513 citations

Hit Papers

Artificial intelligence and machine learning for medical ... 2021 2026 2022 2024 2021 50 100 150 200
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. Artificial intelligence and machine learning for medical imaging: A technology review
    2021 236 citations Ana María Barragán Montero, Umair Javaid et al. Physica Medica profile →

Peers

Paul Desbordes
Wei-Chih Shen Taiwan
Zekun Jiang China
Jiawei Sun China
Michael Z. Liu United States
David Clunie United States
Bas H. M. van der Velden Netherlands
Leona Katsuta Japan
Cai Chang China
Ayelet Akselrod-Ballin Israel
Jincao Yao China
Wei-Chih Shen Taiwan
Paul Desbordes
Citations per year, relative to Paul Desbordes Paul Desbordes (= 1×) peers Wei-Chih Shen

Countries citing papers authored by Paul Desbordes

Since Specialization
Citations

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

Fields of papers citing papers by Paul Desbordes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Desbordes

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

All Works

13 of 13 papers shown
1.
Topalovic, Marko, et al.. (2023). Predicting total lung capacity from spirometry: a machine learning approach. Frontiers in Medicine. 10. 1174631–1174631. 5 indexed citations
2.
3.
4.
Montero, Ana María Barragán, Umair Javaid, Gilmer Valdés, et al.. (2021). Artificial intelligence and machine learning for medical imaging: A technology review. Physica Medica. 83. 242–256. 236 indexed citations breakdown →
5.
François-Lavet, Vincent, et al.. (2021). Reinforcement Learning for Radiotherapy Dose Fractioning Automation. Biomedicines. 9(2). 214–214. 14 indexed citations
6.
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
7.
Desbordes, Paul, et al.. (2019). Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn). IEEE Access. 7. 181789–181799. 27 indexed citations
8.
Decazes, Pierre, Stéphanie Becker, Pierre Véra, et al.. (2018). Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma. European Journal of Nuclear Medicine and Molecular Imaging. 45(10). 1672–1679. 24 indexed citations
9.
Desbordes, Paul, Isabelle Gardin, Pierre Véra, et al.. (2018). Combination of baseline FDG PET/CT total metabolic tumour volume and gene expression profile have a robust predictive value in patients with diffuse large B-cell lymphoma. European Journal of Nuclear Medicine and Molecular Imaging. 45(5). 680–688. 72 indexed citations
10.
Martínez, Mikel, et al.. (2018). Agenesis of Internal Carotid Artery and Ischemic Stroke, One Case Report: A Review of Literature. OALib. 5(10). 1–9. 1 indexed citations
11.
Desbordes, Paul, Su Ruan, Romain Modzelewski, et al.. (2017). Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier. PLoS ONE. 12(3). e0173208–e0173208. 36 indexed citations
12.
Desbordes, Paul, et al.. (2016). Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Computerized Medical Imaging and Graphics. 60. 42–49. 95 indexed citations
13.
Desbordes, Paul, Caroline Petitjean, & Su Ruan. (2015). Segmentation of lymphoma tumor in PET images using cellular automata: A preliminary study. IRBM. 37(1). 3–10. 8 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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2026