Benoît Schmauch

1.7k total citations · 1 hit paper
14 papers, 763 citations indexed

About

Benoît Schmauch is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Benoît Schmauch has authored 14 papers receiving a total of 763 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Artificial Intelligence and 4 papers in Cancer Research. Recurrent topics in Benoît Schmauch's work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (5 papers) and Colorectal Cancer Screening and Detection (3 papers). Benoît Schmauch is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (5 papers) and Colorectal Cancer Screening and Detection (3 papers). Benoît Schmauch collaborates with scholars based in France and United States. Benoît Schmauch's co-authors include Charlie Saillard, Thomas Clozel, Julien Caldéraro, Pierre Courtiol, Matahi Moarii, Mikhail Zaslavskiy, Elodie Pronier, Gilles Wainrib, Alain Luciani and Paul Hérent and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Hepatology.

In The Last Decade

Benoît Schmauch

13 papers receiving 748 citations

Hit Papers

A deep learning model to predict RNA-Seq expression of tu... 2020 2026 2022 2024 2020 50 100 150 200 250
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. A deep learning model to predict RNA-Seq expression of tumours from whole slide images
    2020 282 citations Benoît Schmauch, Alberto Romagnoni et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Benoît Schmauch France 8 448 370 134 126 121 14 763
Charlie Saillard France 6 433 1.0× 379 1.0× 145 1.1× 146 1.2× 120 1.0× 12 754
Pierre Courtiol France 4 418 0.9× 436 1.2× 182 1.4× 164 1.3× 69 0.6× 7 815
Jerome Cheng United States 14 185 0.4× 305 0.8× 58 0.4× 101 0.8× 24 0.2× 49 627
Richard Colling United Kingdom 18 263 0.6× 318 0.9× 122 0.9× 252 2.0× 17 0.1× 47 769
Zeyan Xu China 13 440 1.0× 301 0.8× 70 0.5× 180 1.4× 32 0.3× 42 758
Tokiya Abe Japan 15 89 0.2× 166 0.4× 56 0.4× 176 1.4× 115 1.0× 49 645
Liangqun Lu United States 5 200 0.4× 170 0.5× 255 1.9× 82 0.7× 76 0.6× 7 819
Isabel Schobert Germany 9 410 0.9× 167 0.5× 89 0.7× 127 1.0× 335 2.8× 18 694
Ole-Johan Skrede United Kingdom 3 401 0.9× 319 0.9× 110 0.8× 241 1.9× 11 0.1× 4 654
Marios A. Gavrielides United States 21 737 1.6× 416 1.1× 74 0.6× 215 1.7× 24 0.2× 68 1.2k

Countries citing papers authored by Benoît Schmauch

Since Specialization
Citations

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

Fields of papers citing papers by Benoît Schmauch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benoît Schmauch

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

All Works

14 of 14 papers shown
1.
Fu, Yu, Marie Karanian, Raul Perret, et al.. (2023). Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor. npj Precision Oncology. 7(1). 71–71. 10 indexed citations
2.
Schmauch, Benoît, Sarah Elsoukkary, Amika Moro, et al.. (2023). Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery. Journal of Pathology Informatics. 15. 100360–100360. 6 indexed citations
3.
Schmauch, Benoît, Sarah McIntyre, Patrick Sin‐Chan, et al.. (2023). Machine learning-based multimodal prediction of prognosis in patients with resected intrahepatic cholangiocarcinoma.. Journal of Clinical Oncology. 41(16_suppl). 4121–4121. 1 indexed citations
4.
Terrail, Jean Ogier du, Mathieu Andreux, Charles Maussion, et al.. (2022). Collaborative federated learning behind hospitals’ firewalls for predicting histological complete response to neoadjuvant chemotherapy in triple-negative breast cancer.. Journal of Clinical Oncology. 40(16_suppl). 590–590. 1 indexed citations
5.
Garberis, Ingrid, Charlie Saillard, Damien Drubay, et al.. (2021). 1124O Prediction of distant relapse in patients with invasive breast cancer from deep learning models applied to digital pathology slides. Annals of Oncology. 32. S921–S921. 3 indexed citations
6.
Saillard, Charlie, Benoît Schmauch, Magali Svrcek, et al.. (2021). Identification of pancreatic adenocarcinoma molecular subtypes on histology slides using deep learning models.. Journal of Clinical Oncology. 39(15_suppl). 4141–4141.
7.
Saillard, Charlie, Benoît Schmauch, Matahi Moarii, et al.. (2020). Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides. Hepatology. 72(6). 2000–2013. 200 indexed citations
8.
Saillard, Charlie, Benoît Schmauch, Matahi Moarii, et al.. (2020). Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides. Journal of Hepatology. 73. S381–S381. 14 indexed citations
9.
Schmauch, Benoît, Alberto Romagnoni, Elodie Pronier, et al.. (2020). A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nature Communications. 11(1). 3877–3877. 282 indexed citations breakdown →
10.
Pronier, Elodie, Benoît Schmauch, Alberto Romagnoni, et al.. (2020). Abstract 2105: HE2RNA: A deep learning model for transcriptomic learning from digital pathology. Cancer Research. 80(16_Supplement). 2105–2105. 2 indexed citations
11.
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
12.
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
13.
Devillers, Laurence, et al.. (2018). Speech Emotion Recognition with Data Augmentation and Layer-wise Learning Rate Adjustment.. arXiv (Cornell University). 12 indexed citations
14.
Schmauch, Benoît, et al.. (2016). Deep learning approach for diabetic retinopathy screening. Acta Ophthalmologica. 94(S256). 35 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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