Search Results for author:
Found 6 papers, 1 papers with code
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
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.
Parallel transport in shape analysis: a scalable numerical scheme
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake.
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
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.
Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs.
Weakly-supervised positional contrastive learning: application to cirrhosis classification
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e. g., radiological scores).
Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR).
