Simon Jégou

2.7k total citations · 1 hit paper
13 papers, 1.5k citations indexed

About

Simon Jégou is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Simon Jégou has authored 13 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Oncology and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Simon Jégou's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Medical Imaging Techniques and Applications (2 papers) and Neuroendocrine Tumor Research Advances (2 papers). Simon Jégou is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Medical Imaging Techniques and Applications (2 papers) and Neuroendocrine Tumor Research Advances (2 papers). Simon Jégou collaborates with scholars based in France, United States and Switzerland. Simon Jégou's co-authors include Yoshua Bengio, Michal Drozdzal, Adriana Romero, David Vázquez, Paul Jehanno, Paul Hérent, Olivier Dehaene, Charlie Saillard, Benoît Schmauch and Alain Luciani and has published in prestigious journals such as European Journal of Nuclear Medicine and Molecular Imaging, Modern Pathology and European Radiology.

In The Last Decade

Simon Jégou

13 papers receiving 1.5k citations

Hit Papers

align trajectories

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.

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

2017 1.1k citations Simon Jégou, Michal Drozdzal et al. PolyPublie (École Polytechnique de Montréal) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Simon Jégou France	8	640	549	381	175	158	13	1.5k
Sidike Paheding United States	11	540 0.8×	350 0.6×	309 0.8×	212 1.2×	144 0.9×	36	1.6k
Moulay A. Akhloufi Canada	30	1.2k 1.8×	582 1.1×	590 1.5×	100 0.6×	150 0.9×	173	2.7k
Nahian Siddique United States	8	431 0.7×	326 0.6×	270 0.7×	193 1.1×	91 0.6×	12	1.3k
Vasileios Belagiannis Germany	18	1.1k 1.7×	533 1.0×	815 2.1×	225 1.3×	125 0.8×	49	2.1k
Mei Chen China	22	508 0.8×	302 0.6×	739 1.9×	172 1.0×	96 0.6×	87	1.8k
Qiaowei Zhang China	14	881 1.4×	825 1.5×	512 1.3×	436 2.5×	205 1.3×	35	2.1k
Huimin Huang China	16	1.0k 1.6×	571 1.0×	649 1.7×	282 1.6×	228 1.4×	71	2.5k
Colin Elkin United States	6	433 0.7×	337 0.6×	268 0.7×	191 1.1×	109 0.7×	8	1.3k
Guangmin Sun China	20	406 0.6×	301 0.5×	280 0.7×	128 0.7×	132 0.8×	148	1.3k
Supun Samarasekera United States	23	1.7k 2.6×	519 0.9×	294 0.8×	286 1.6×	199 1.3×	82	2.6k

Countries citing papers authored by Simon Jégou

Since Specialization

Citations

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

Fields of papers citing papers by Simon Jégou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Jégou

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

All Works

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13 of 13 papers shown

Jacob, Paul, Côme Lepage, Claire Gallois, et al.. (2025). Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer. Modern Pathology. 38(11). 100877–100877. 1 indexed citations

Said, Daniela, Daniel Stocker, Stefanie J. Hectors, et al.. (2023). Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks. European Radiology. 33(9). 6020–6032. 12 indexed citations

Pavel, Marianne, Clarisse Dromain, Maxime Ronot, et al.. (2023). The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors. Future Oncology. 19(32). 2185–2199. 5 indexed citations

Dromain, Clarisse, Marianne Pavel, Maxime Ronot, et al.. (2023). Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors. Future Oncology. 19(32). 2171–2183. 5 indexed citations

Barrallier, Laurent, et al.. (2022). Mechanical behavior of polycrystals: Coupled in situ DIC-EBSD analysis of pure copper under tensile test. Materials Characterization. 194. 112322–112322. 9 indexed citations

Jégou, Simon, et al.. (2021). Towards an image quality criterion to optimize Digital image correlation. Use of an analytical model to optimize acquisition conditions. Optics & Laser Technology. 148. 107792–107792. 3 indexed citations

Blanc‐Durand, Paul, Simon Jégou, Salim Kanoun, et al.. (2020). Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.. European Journal of Nuclear Medicine and Molecular Imaging. 48(5). 1362–1370. 94 indexed citations

Blanc‐Durand, Paul, Jean-Baptiste Schiratti, Klamer Schutte, et al.. (2020). Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment. Diagnostic and Interventional Imaging. 101(12). 789–794. 40 indexed citations

Blanc‐Durand, Paul, Luca Campedel, Sebastien Mulé, et al.. (2020). Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer. European Radiology. 30(6). 3528–3537. 23 indexed citations

10.

Hérent, Paul, Benoît Schmauch, Paul Jehanno, et al.. (2019). Detection and characterization of MRI breast lesions using deep learning. Diagnostic and Interventional Imaging. 100(4). 219–225. 82 indexed citations

11.

Schmauch, Benoît, Paul Hérent, Paul Jehanno, et al.. (2019). Diagnosis of focal liver lesions from ultrasound using deep learning. Diagnostic and Interventional Imaging. 100(4). 227–233. 115 indexed citations

12.

Zaslavskiy, Mikhail, Simon Jégou, Eric W. Tramel, & Gilles Wainrib. (2018). ToxicBlend: Virtual screening of toxic compound with ensemble predictors. Toxicology Letters. 295. S98–S98. 3 indexed citations

13.

Jégou, Simon, Michal Drozdzal, David Vázquez, Adriana Romero, & Yoshua Bengio. (2017). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. PolyPublie (École Polytechnique de Montréal). 1175–1183. 1145 indexed citations breakdown →

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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