Jérôme Lapuyade‐Lahorgue
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Aerospace Engineering
- Co-authors
- Su RuanWojciech PieczynskiFrédéric BarbarescoPierre LanchantinJing‐Hao XueNicolas BrunelPierre VéraPierre Decazes
- Topics
- Radiomics and Machine Learning in Medical Imaging (9 papers)Medical Imaging Techniques and Applications (6 papers)Bayesian Methods and Mixture Models (6 papers)
- Partner nations
- FranceSwitzerlandUnited States
In The Last Decade
Jérôme Lapuyade‐Lahorgue
22 papers receiving 358 citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 135
- Radiology, Nuclear Medicine and Imaging 95
- Computer Vision and Pattern Recognition 92
- Signal Processing 52
- Aerospace Engineering 44
Countries citing papers authored by Jérôme Lapuyade‐Lahorgue
This map shows the geographic impact of Jérôme Lapuyade‐Lahorgue'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 Jérôme Lapuyade‐Lahorgue with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jérôme Lapuyade‐Lahorgue more than expected).
Fields of papers citing papers by Jérôme Lapuyade‐Lahorgue
This network shows the impact of papers produced by Jérôme Lapuyade‐Lahorgue. 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 Jérôme Lapuyade‐Lahorgue. The network helps show where Jérôme Lapuyade‐Lahorgue may publish in the future.
Co-authorship network of co-authors of Jérôme Lapuyade‐Lahorgue
This figure shows the co-authorship network connecting the top 25 collaborators of Jérôme Lapuyade‐Lahorgue. A scholar is included among the top collaborators of Jérôme Lapuyade‐Lahorgue 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 Jérôme Lapuyade‐Lahorgue. Jérôme Lapuyade‐Lahorgue is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewbreakdown → | 116 |
| 5 | 9 | |
| 6 | 6 | |
| 7 | 28 | |
| 8 | 24 | |
| 9 | 8 | |
| 10 | 8 | |
| 11 | Clinical MR-based attenuation correction using continuous linear attenuation coefficients derived from a simple Dixon-like sequence | 1 |
| 12 | 2 | |
| 13 | 4 | |
| 14 | 5 | |
| 15 | 19 | |
| 16 | 32 | |
| 17 | 3 | |
| 18 | 17 | |
| 19 | 57 | |
| 20 | 4 |
About Jérôme Lapuyade‐Lahorgue
Jérôme Lapuyade‐Lahorgue is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Radiology, Nuclear Medicine and Imaging, having authored 23 papers that have together received 375 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Medical Imaging Techniques and Applications (6 papers) and Bayesian Methods and Mixture Models (6 papers). The work is most often cited by research in Health Informatics (10 citations), Signal Processing (52 citations) and Computer Vision and Pattern Recognition (92 citations). Jérôme Lapuyade‐Lahorgue has collaborated with scholars based in France, Switzerland and United States. Frequent co-authors include Su Ruan, Wojciech Pieczynski, Frédéric Barbaresco, Pierre Lanchantin, Jing‐Hao Xue, Nicolas Brunel, Pierre Véra, Pierre Decazes, Isabelle Gardin and Yuntao Yu. Their work appears in journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Image Processing and Medical Physics.
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.