Équipe IMAGeS : Images, Modélisation, Apprentissage, Géométrie et Statistique

Différences entre les versions de « HealthTech Advanced Medical Image Processing: methods »

De Équipe IMAGeS : Images, Modélisation, Apprentissage, Géométrie et Statistique
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(Page créée avec « === Objectives === To introduce advanced processing issues specific to certain medical image imaging modalities (MRI, diffusion MRI, functional MRI, elastography) To g... »)
 
 
(2 versions intermédiaires par le même utilisateur non affichées)
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=== Objectives ===
 
=== Objectives ===
To introduce advanced processing issues specific to certain medical image imaging modalities (MRI, diffusion MRI, functional MRI, elastography)
+
 
To give an overview of the specificities related to application (clinical routine, preclinical imaging).
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To introduce advanced image processing methods applied in the specific context of biomedical imaging
 
To implement image processing methods applied to medical images via a scientific programming language and the use of dedicated software
 
To implement image processing methods applied to medical images via a scientific programming language and the use of dedicated software
  
 +
=== Lectures ===
  
=== Lectures ===
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* Reconstruction and inverse problems (D. Fortun, 4h)
Lectures
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* Radiomics (F. Ouhmich, 4h)
Diffusion MRI (Vincent Noblet): materials
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* Change detection (V. Noblet, 4h) [[https://seafile.unistra.fr/d/bb8ae92d950042d999ca/ Materials]]
Elastography (Jonathan Vappou)
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* NeuroImaging group study (M. Mondino, 4h)  
fMRI (Céline Meillier)
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* Dimensionality reduction techniques and manifold learning (S. Faisan, 4h)
Preclinical imaging (Chrystelle Po)
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* Graph theory and connectivity analysis (M. Sourty, 4h)
Insights from the radiologist (Mickaël Ohana)
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* Mathematical morphology (B. Naegel, 6h)
Insights from the radiotherapist (Philippe Meyer)
 

Version actuelle datée du 27 novembre 2023 à 23:49

Objectives

To introduce advanced image processing methods applied in the specific context of biomedical imaging To implement image processing methods applied to medical images via a scientific programming language and the use of dedicated software

Lectures

  • Reconstruction and inverse problems (D. Fortun, 4h)
  • Radiomics (F. Ouhmich, 4h)
  • Change detection (V. Noblet, 4h) [Materials]
  • NeuroImaging group study (M. Mondino, 4h)
  • Dimensionality reduction techniques and manifold learning (S. Faisan, 4h)
  • Graph theory and connectivity analysis (M. Sourty, 4h)
  • Mathematical morphology (B. Naegel, 6h)