Nicolas Duchateau
Scholar

Nicolas Duchateau

Google Scholar ID: PzKbssMAAAAJ
Associate Professor / CREATIS lab - Université Lyon 1, France
Medical image analysisComputational anatomyCardiac imaging
Citations & Impact
All-time
Citations
1,751
 
H-index
18
 
i10-index
26
 
Publications
20
 
Co-authors
44
list available
Resume (English only)
Academic Achievements
  • - Published multiple academic papers, including:
  • - 'Estimation of segmental longitudinal strain in transesophageal echocardiography by deep learning' in IEEE Journal of Biomedical and Health Informatics
  • - 'PRIME 2.0: An update to the proposed requirements for cardiovascular imaging-related machine learning evaluation checklist' in JACC Cardiovascular Imaging
  • - 'Fusing echocardiography images and medical records for continuous patient stratification' in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
  • - 'Comparison of synthetic LGE with optimal inversion time vs. conventional LGE via representation learning: quantification of bias in population analysis' in Computers in Biology and Medicine
  • - 'Hierarchical data integration with Gaussian processes: application to the characterization of cardiac ischemia-reperfusion patterns' in IEEE Transactions on Medical Imaging
  • - 'Detailed evaluation of a population-wise personalization approach to generate synthetic myocardial infarct images' in Pattern Recognition Letters
Research Experience
  • - Associate Professor at CREATIS lab, Université Lyon 1
  • - Current team includes several PhD students working on various research topics related to cardiac imaging, shape, and deformation
  • - Former team members include postdocs and PhD students with diverse research interests
Background
  • - Associate Professor at Université Lyon 1
  • - Member of CREATIS lab
  • - Junior member of Institut Universitaire de France
  • - Associate Editor for Neurocomputing journal
  • - Research focus: Characterization of diseases from medical imaging populations
  • - Methodological side: New computational approaches based on statistical atlases and machine learning
  • - Applicative side: Cardiac function and widespread imaging modalities such as echocardiography and magnetic resonance