Guillaume Jaume
- Health Informatics top 1%
- Biophysics top 5%
- Cell Image Analysis Techniques 3
- Artificial Intelligence top 2%
- AI in cancer detection 12
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- Radiomics and Machine Learning in Medical Imaging 5
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- Digital Imaging for Blood Diseases 5
- Handwritten Text Recognition Techniques 1
- Generative Adversarial Networks and Image Synthesis 1
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- Biomedical Text Mining and Ontologies 3
- Gene expression and cancer classification 2
- Co-authors
- Faisal MahmoodDrew F. K. WilliamsonMing Y. LuRichard J. ChenAnurag VaidyaAndrew H. SongTong DingBowen Chen
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Guillaume Jaume
13 papers receiving 868 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Health Informatics 96
- Biophysics 107
- Artificial Intelligence 577
- Radiology, Nuclear Medicine and Imaging 401
- Computer Vision and Pattern Recognition 210
Countries citing papers authored by Guillaume Jaume
This map shows the geographic impact of Guillaume Jaume'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 Guillaume Jaume with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guillaume Jaume more than expected).
Fields of papers citing papers by Guillaume Jaume
This network shows the impact of papers produced by Guillaume Jaume. 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 Guillaume Jaume. The network helps show where Guillaume Jaume may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guillaume Jaume, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 27 | |
| 2 | A visual-language foundation model for computational pathologybreakdown → | 2024 | 228 |
| 3 | Towards a general-purpose foundation model for computational pathologybreakdown → | 2024 | 331 |
| 4 | 2024 | 10 | |
| 5 | 2024 | 32 | |
| 6 | 2024 | 39 | |
| 7 | 2024 | 16 | |
| 8 | 2024 | 0 | |
| 9 | 2023 | 17 | |
| 10 | 2023 | 12 | |
| 11 | Artificial intelligence for digital and computational pathologybreakdown → | 2023 | 103 |
| 12 | 2022 | 65 | |
| 13 | Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology. | 2021 | 3 |
| 14 | 2017 | 2 |
About Guillaume Jaume
Guillaume Jaume is a scholar working on Health Informatics, Biophysics and Artificial Intelligence, having authored 14 papers that have together received 885 indexed citations. Recurring topics across this work include AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Digital Imaging for Blood Diseases (5 papers), Biomedical Text Mining and Ontologies (3 papers), Cell Image Analysis Techniques (3 papers), Gene expression and cancer classification (2 papers), Handwritten Text Recognition Techniques (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Health Informatics (96 citations), Biophysics (107 citations) and Artificial Intelligence (577 citations). Guillaume Jaume has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Faisal Mahmood, Drew F. K. Williamson, Ming Y. Lu, Richard J. Chen, Anurag Vaidya, Andrew H. Song, Tong Ding, Bowen Chen, Andrew Zhang and Long P. Le.
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.