8 citations · 9 across the 4 of their papers we have counts for
4 papers
Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +9
T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is cha…
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Guillaume Morel, Lucas Drumetz, Simon Benaïchouche +2
Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities. A now well estab…
Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks
Pierre-Henri Conze, Ali Emre Kavur, Emilie Cornec-Le Gall +4
Objective : Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automate…
End-to-end learning of energy-based representations for irregularly-sampled signals and images
Ronan Fablet, Lucas Drumetz, François Rousseau
For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often involve irregular space-time sampling p…