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Poster

Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data

Stéphanie ALLASSONNIERE · Juliette Chevallier · Stephane Oudard

Pacific Ballroom #197

Keywords: [ MCMC ] [ Stochastic Methods ] [ Computational Biology and Bioinformatics ] [ Hierarchical Models ] [ Latent Variable Models ]


Abstract:

We introduce a hierarchical model which allows to estimate a group-average piecewise-geodesic trajectory in the Riemannian space of measurements and individual variability. This model falls into the well defined mixed-effect models. The subject-specific trajectories are defined through spatial and temporal transformations of the group-average piecewise-geodesic path, component by component. Thus we can apply our model to a wide variety of situations. Due to the non-linearity of the model, we use the Stochastic Approximation Expectation-Maximization algorithm to estimate the model parameters. Experiments on synthetic data validate this choice. The model is then applied to the metastatic renal cancer chemotherapy monitoring: we run estimations on RECIST scores of treated patients and estimate the time they escape from the treatment. Experiments highlight the role of the different parameters on the response to treatment.

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