We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR).
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e. g., radiological scores).
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs.
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.
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.
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake.