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Seminar

Tuesday, May 4th, 2021
From 13h To 14h
Centre de recherche - Orsay - Webinar

Clustering of longitudinal shape data sets into mixtures of independent or branching trajectories

This presentation aims at introducing how we model the evolution of a population given repeated observations of several subjects over time, i.e. a longitudinal data set. Our method learns an average trajectory (or representative curve) from images, shapes or other inputs and its variance in space and time. Representative trajectories are built as the combination of pieces of curves. This enables to account for changes in dynamic for example relapse events in oncology.  We also handle heterogeneous populations. For this we use mixture model which are flexible enough to handle independent trajectories for each cluster as well as fork and merge scenarios. The estimation of such non linear mixture models in high dimension is known to be difficult because of the trapping states effect that hampers the optimisation of cluster assignments during training. We address this issue by using a tempered version of the stochastic EM algorithm. We apply our algorithm on different data sets: 1D RECIST score used to monitor tumors growth and meshes of the hippocampus. In particular, we show how the method can be used to test different scenarios of hippocampus atrophy in aging by using an heterogeneous population of normal aging individuals and mild cognitive impaired subjects.

 

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Speaker(s)

Prof. Stéphanie Allassonnière
Professor of Applied Mathematics, PR[AI]RIE fellow and deputy director

Université de Paris, Ecole Polytechnique

Hosted by

Mrs. Frédérique Frouin
Chercheure
U1288 – LITO

Institut Curie

Invited by

Dr. Stéphanie Jehan-Besson
Chercheure CNRS
U1288 – LITO

Institut Curie

Contact

Mrs. Frédérique Frouin

Chercheure

Institut Curie

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