Guillaume Jaume

2.6k total citations · 3 hit papers
14 papers, 885 citations indexed

About

Guillaume Jaume is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Guillaume Jaume has authored 14 papers receiving a total of 885 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Guillaume Jaume's work include AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Digital Imaging for Blood Diseases (5 papers). Guillaume Jaume is often cited by papers focused on AI in cancer detection (12 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Digital Imaging for Blood Diseases (5 papers). Guillaume Jaume collaborates with scholars based in United States, Switzerland and United Kingdom. Guillaume Jaume's co-authors include Faisal Mahmood, Drew F. K. Williamson, Ming Y. Lu, Richard J. Chen, Anurag Vaidya, Andrew H. Song, Tong Ding, Andrew Zhang, Bowen Chen and Long P. Le and has published in prestigious journals such as Cell, Nature Medicine and Medical Image Analysis.

In The Last Decade

Guillaume Jaume

13 papers receiving 868 citations

Hit Papers

align trajectories sort by overperformance

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.

Towards a general-purpose foundation model for computational pathology

2024 331 citations Richard J. Chen, Tong Ding et al. Nature Medicine profile →
A visual-language foundation model for computational pathology

2024 228 citations Ming Y. Lu, Bowen Chen et al. Nature Medicine profile →
Artificial intelligence for digital and computational pathology

2023 103 citations Andrew H. Song, Guillaume Jaume et al. Nature Reviews Bioengineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Guillaume Jaume United States	11	577	401	210	124	114	14	885
Chengkuan Chen United States	4	488 0.8×	372 0.9×	177 0.8×	113 0.9×	81 0.7×	5	807
Nikolas Stathonikos Netherlands	14	508 0.9×	328 0.8×	163 0.8×	78 0.6×	124 1.1×	32	687
Anurag Vaidya United States	8	480 0.8×	407 1.0×	117 0.6×	130 1.0×	104 0.9×	10	865
Mane Williams United States	5	431 0.7×	338 0.8×	110 0.5×	127 1.0×	107 0.9×	6	707
Maschenka Balkenhol Netherlands	13	685 1.2×	530 1.3×	298 1.4×	70 0.6×	151 1.3×	21	906
Pooya Mobadersany United States	5	432 0.7×	420 1.0×	94 0.4×	114 0.9×	105 0.9×	9	716
Péter Bándi Netherlands	8	617 1.1×	421 1.0×	292 1.4×	61 0.5×	116 1.0×	15	779
Andrew Zhang United States	4	362 0.6×	265 0.7×	120 0.6×	88 0.7×	84 0.7×	6	619
Lily H. Peng United States	6	446 0.8×	376 0.9×	96 0.5×	77 0.6×	93 0.8×	6	689
Dyke Ferber Germany	11	579 1.0×	507 1.3×	138 0.7×	129 1.0×	386 3.4×	23	1.1k

Countries citing papers authored by Guillaume Jaume

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Guillaume Jaume

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

All Works

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14 of 14 papers shown

Song, Andrew H., Mane Williams, Drew F. K. Williamson, et al.. (2024). Analysis of 3D pathology samples using weakly supervised AI. Cell. 187(10). 2502–2520.e17. 27 indexed citations

Lu, Ming Y., Bowen Chen, Drew F. K. Williamson, et al.. (2024). A visual-language foundation model for computational pathology. Nature Medicine. 30(3). 863–874. 228 indexed citations breakdown →

Vaidya, Anurag, Richard J. Chen, Drew F. K. Williamson, et al.. (2024). Demographic bias in misdiagnosis by computational pathology models. Nature Medicine. 30(4). 1174–1190. 39 indexed citations

Chen, Richard J., Tong Ding, Ming Y. Lu, et al.. (2024). Towards a general-purpose foundation model for computational pathology. Nature Medicine. 30(3). 850–862. 331 indexed citations breakdown →

Jaume, Guillaume, et al.. (2024). Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction. 11579–11590. 32 indexed citations

Jaume, Guillaume, Anurag Vaidya, Richard J. Chen, et al.. (2024). Transcriptomics-Guided Slide Representation Learning in Computational Pathology. 9632–9644. 10 indexed citations

Chen, Richard H., Guillaume Jaume, Ahrong Kim, et al.. (2024). HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis. 53798–53833.

Song, Andrew H., Richard J. Chen, Tong Ding, et al.. (2024). Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. 11566–11578. 16 indexed citations

Pati, Pushpak, et al.. (2023). Weakly supervised joint whole-slide segmentation and classification in prostate cancer. Medical Image Analysis. 89. 102915–102915. 17 indexed citations

10.

Jaume, Guillaume, et al.. (2023). Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images. 1–4. 12 indexed citations

11.

Song, Andrew H., Guillaume Jaume, Drew F. K. Williamson, et al.. (2023). Artificial intelligence for digital and computational pathology. Nature Reviews Bioengineering. 1(12). 930–949. 103 indexed citations breakdown →

12.

Brancati, Nadia, Anna Maria Anniciello, Pushpak Pati, et al.. (2022). BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images. Database. 2022. 65 indexed citations

13.

Pati, Pushpak, Guillaume Jaume, Florinda Feroce, et al.. (2021). Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology.. arXiv (Cornell University). 3 indexed citations

14.

Foncubierta–Rodríguez, Antonio, et al.. (2017). Interpreting Data from Scanned Tables. 5–6. 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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