Jérôme Schmid

648 total citations
30 papers, 419 citations indexed

About

Jérôme Schmid is a scholar working on Surgery, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jérôme Schmid has authored 30 papers receiving a total of 419 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Surgery, 11 papers in Biomedical Engineering and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jérôme Schmid's work include Radiomics and Machine Learning in Medical Imaging (6 papers), Orthopaedic implants and arthroplasty (6 papers) and Medical Image Segmentation Techniques (5 papers). Jérôme Schmid is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), Orthopaedic implants and arthroplasty (6 papers) and Medical Image Segmentation Techniques (5 papers). Jérôme Schmid collaborates with scholars based in Switzerland, United States and Singapore. Jérôme Schmid's co-authors include Nadia Magnenat‐Thalmann, Jinman Kim, Caecilia Charbonnier, Chung‐Kwong Yeung, Yannick Bailly, Sylvain Chagué, Panayiotis Christofilopoulos, Karen Kinkel, Frank C. Kolo and Christian Lovis and has published in prestigious journals such as Medical Physics, Review of Scientific Instruments and Medical Image Analysis.

In The Last Decade

Jérôme Schmid

28 papers receiving 410 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jérôme Schmid Switzerland 11 212 163 108 59 39 30 419
M. de la Fuente Germany 13 312 1.5× 172 1.1× 84 0.8× 74 1.3× 38 1.0× 67 557
Nóra Baka Netherlands 12 149 0.7× 219 1.3× 159 1.5× 157 2.7× 39 1.0× 21 457
Yuta Hiasa Japan 9 111 0.5× 119 0.7× 71 0.7× 79 1.3× 17 0.4× 15 282
Marcel Lüthi Switzerland 11 129 0.6× 143 0.9× 151 1.4× 46 0.8× 11 0.3× 23 405
Toshihiko Sasama Japan 11 272 1.3× 184 1.1× 150 1.4× 105 1.8× 7 0.2× 34 493
Justus Schock Germany 10 106 0.5× 87 0.5× 84 0.8× 98 1.7× 7 0.2× 28 378
Darko Štern Austria 17 216 1.0× 407 2.5× 157 1.5× 221 3.7× 94 2.4× 34 960
Raphael Schwarz Germany 12 142 0.7× 178 1.1× 50 0.5× 42 0.7× 20 0.5× 30 541
Sandro-Michael Heining Germany 8 213 1.0× 165 1.0× 240 2.2× 30 0.5× 8 0.2× 22 452
Yuki Kato Japan 11 83 0.4× 66 0.4× 32 0.3× 48 0.8× 7 0.2× 51 307

Countries citing papers authored by Jérôme Schmid

Since Specialization
Citations

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

Fields of papers citing papers by Jérôme Schmid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jérôme Schmid

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

All Works

20 of 20 papers shown
1.
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Goldman, Jean-Philippe, et al.. (2025). Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification. Computers in Biology and Medicine. 188. 109721–109721. 5 indexed citations
3.
Mendes, Lisa A., et al.. (2025). Characterization of fluid in facial sinuses on post-mortem CT in case of death by drowning. International Journal of Legal Medicine. 139(5). 2233–2240.
4.
Kinkel, Karen, et al.. (2024). Efficient Clinical Information Extraction from Breast Radiology Reports in French. Studies in health technology and informatics. 316. 1780–1784. 1 indexed citations
5.
Schmid, Jérôme, et al.. (2023). A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images. European Radiology Experimental. 7(1). 39–39. 4 indexed citations
6.
7.
Schmid, Jérôme, et al.. (2020). Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation. Journal of Medical Imaging. 7(1). 1–1. 5 indexed citations
8.
Huang, Robin, David F. Fletcher, Narinder Singh, et al.. (2019). An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography. Computers in Biology and Medicine. 115. 103505–103505. 11 indexed citations
9.
Lerch, Till D., et al.. (2019). Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration. International Journal of Computer Assisted Radiology and Surgery. 14(3). 545–561. 29 indexed citations
10.
Schmid, Jérôme, et al.. (2015). MyHip: supporting planning and surgical guidance for a better total hip arthroplasty. International Journal of Computer Assisted Radiology and Surgery. 10(10). 1547–1556. 14 indexed citations
11.
Charbonnier, Caecilia, et al.. (2014). Analysis of Hip Range of Motion in Everyday Life: A Pilot Study. Hip International. 25(1). 82–90. 29 indexed citations
12.
Schmid, Jérôme, Jinman Kim, & Nadia Magnenat‐Thalmann. (2010). Robust statistical shape models for MRI bone segmentation in presence of small field of view. Medical Image Analysis. 15(1). 155–168. 73 indexed citations
13.
Schmid, Jérôme, Jinman Kim, & Nadia Magnenat‐Thalmann. (2010). Extreme leg motion analysis of professional ballet dancers via MRI segmentation of multiple leg postures. International Journal of Computer Assisted Radiology and Surgery. 6(1). 47–57. 5 indexed citations
14.
Schmid, Jérôme, Jinman Kim, & Nadia Magnenat‐Thalmann. (2010). Coupled Registration-Segmentation: Application to Femur Analysis with Intra-subject Multiple Levels of Detail MRI Data. Lecture notes in computer science. 13(Pt 2). 562–569. 1 indexed citations
15.
Schmid, Jérôme, José A. Iglesias-Guitián, Enrico Gobbetti, & Nadia Magnenat‐Thalmann. (2010). A GPU framework for parallel segmentation of volumetric images using discrete deformable models. The Visual Computer. 27(2). 85–95. 12 indexed citations
16.
Schmid, Jérôme & Nadia Magnenat‐Thalmann. (2008). MRI Bone Segmentation Using Deformable Models and Shape Priors. Lecture notes in computer science. 11(Pt 1). 119–126. 69 indexed citations
17.
Magnenat‐Thalmann, Nadia, Caecilia Charbonnier, & Jérôme Schmid. (2008). Multimedia application to the simulation of human musculoskeletal system: A visual lower limb model from multimodal captured data. 520–525. 3 indexed citations
18.
Schmid, Jérôme, et al.. (2007). Advanced da Vinci surgical system simulator for surgeon training and operation planning. International Journal of Medical Robotics and Computer Assisted Surgery. 3(3). 245–251. 54 indexed citations
19.
Dittrich, R., D. Gogl, S. Lammers, et al.. (2006). Signal-Margin-Screening for Multi-Mb MRAM. 467–476. 7 indexed citations
20.
Soler, Luc, Stéphane Nicolau, Jérôme Schmid, et al.. (2005). Virtual Reality and Augmented Reality in Digestive Surgery. HAL (Le Centre pour la Communication Scientifique Directe). 278–279. 25 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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