Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Résumé
We introduce a hierarchical model which allows to estimate both a group-representative piecewise-geodesic trajectory in the Riemannian space of shape and inter-individual variability. Following the approach of Schiratti et al. (NIPS, 2015), we estimate a representative piecewise-geodesic trajectory of the global progression and together with spacial and temporal inter-individual variabilities. We first introduce our model in its most generic formulation and then make it explicit for RECIST (Therasse et al., JNCI, 2000) score monitoring, i.e. for one-dimension manifolds and piecewise-logistically distributed data.
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