François Lauze

52 papers receiving 955 citations

Hit Papers

Deep Feature Learning for Knee Cartilage Segmentation Usi...20132026201720212013100200300

Peers

François Lauze
Comparison fields: 5 of 122
  • Computer Vision and Pattern Recognition 467
  • Radiology, Nuclear Medicine and Imaging 264
  • Artificial Intelligence 198
  • Biomedical Engineering 143
  • Aerospace Engineering 78
Replace Roland Kwitt with:
Roland Kwitt Austria
Christian Roux France
Byung‐Woo Hong South Korea
Mikaël Rousson United States
Hien Van Nguyen United States
H. Siegfried Stiehl Germany
Christian Ronse France
Martin Lillholm Denmark
Gareth Funka-Lea United States
François Lauze relative to Roland Kwitt Austria Roland Kwitt's profile →
Citations per field
00.5×1.5×
Roland Kwitt · 1×
Citations per year

Countries citing papers authored by François Lauze

Since Specialization
Citations

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

Fields of papers citing papers by François Lauze

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of François Lauze

This figure shows the co-authorship network connecting the top 25 collaborators of François Lauze. A scholar is included among the top collaborators of François Lauze 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 François Lauze. François Lauze 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
#WorkIndexed citations
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Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Networkbreakdown →
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10 30
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The Differential of the Exponential Map, Jacobi Fields and Exact Principal Geodesic Analysis
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De l'intérêt de la texture pour la segmentation du visage
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About François Lauze

François Lauze is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Geometry and Topology, having authored 55 papers that have together received 989 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (15 papers), Morphological variations and asymmetry (8 papers) and Medical Image Segmentation Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (467 citations), Radiology, Nuclear Medicine and Imaging (264 citations) and Computer Graphics and Computer-Aided Design (37 citations). François Lauze has collaborated with scholars based in Denmark, France and Netherlands. Frequent co-authors include Mads Nielsen, Erik B. Dam, Christian Igel, Kersten Petersen, Søren Hauberg, Aasa Feragen, Kim Steenstrup Pedersen, Stefan Sommer, Marco Loog and Sune H. Keller. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and International Journal of Computer Vision.

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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