51 citations · 76 across the 8 of their papers we have counts for
7 papers · 1 filter
Weakly-supervised positional contrastive learning: application to cirrhosis classification
Emma Sarfati, Alexandre Bône, Marc-Michel Rohé +2
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histolog…
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies
Emma Sarfati, Alexandre Bone, Marc-Michel Rohe +2
Identifying cirrhosis is key to correctly assess the health of the liver. However, the gold standard diagnosis of the cirrhosis needs a medical intervention to obtain the histologi…
Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
Rebeca Vétil, Clément Abi Nader, Alexandre Bône +4
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a comm…
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Anupama Goparaju, Alexandre Bone, Nan Hu +8
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technolog…
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
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. The…
Parallel transport in shape analysis: a scalable numerical scheme
Maxime Louis, Alexandre Bône, Benjamin Charlier +1
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake. This complexity arises from the always-in…