no code implementations • 23 Nov 2017 • Alexandre Bône, Maxime Louis, Alexandre Routier, Jorge Samper, Michael Bacci, Benjamin Charlier, Olivier Colliot, Stanley Durrleman
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data.
no code implementations • 23 Nov 2017 • Maxime Louis, Alexandre Bône, Benjamin Charlier, Stanley Durrleman
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake.
no code implementations • CVPR 2018 • Alexandre Bône, Olivier Colliot, Stanley Durrleman
We propose a method to learn a distribution of shape trajectories from longitudinal data, i. e. the collection of individual objects repeatedly observed at multiple time-points.
no code implementations • 21 Oct 2022 • Rebeca Vétil, Clément Abi Nader, Alexandre Bône, Marie-Pierre Vullierme, Marc-Michel Roheé, Pietro Gori, Isabelle Bloch
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs.
1 code implementation • 10 Jul 2023 • Emma Sarfati, Alexandre Bône, Marc-Michel Rohé, Pietro Gori, Isabelle Bloch
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e. g., radiological scores).
no code implementations • 22 Aug 2023 • Rebeca Vétil, Clément Abi-Nader, Alexandre Bône, Marie-Pierre Vullierme, Marc-Michel Rohé, Pietro Gori, Isabelle Bloch
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR).