MRI Gastric Images Processing using a Multiobjective Fly Algorithm

Allbwn ymchwil: Cyfraniad at gynhadleddPapur

Fersiynau electronig


  • Shatha Al-Maliki
  • Evelyne Lutton
  • Francois Boue
    Centre National de la Recherche Scientifique (CNRS)
  • Franck Vidal
When dealing with rare and sparse data, like the ones collected during a long and expensive experimental process, machine learning is used in a different perspective. In this context, optimisation-based approaches combined with user visualisation and interactions are sometimes the best way to cope with modelling issues. We present here an example related to an experimental project aiming at understanding the kinetics of gastric emptying using MRI images of the stomach of healthy volunteers. We show how a cooperation/co-evolution algorithm, the ``Fly Algorithm'', can be made multi-objective, and its output, a complex Pareto Front, analysed using interactive Information Visualization (InfoVis) and clustering.
Iaith wreiddiolSaesneg
StatwsCyhoeddwyd - 8 Medi 2018
DigwyddiadEvolutionary Machine Learning - University of Coimbra, Coimbra, Portiwgal
Hyd: 8 Sep 2018 → …


GweithdyEvolutionary Machine Learning
Teitl crynoEvoML
Cyfnod8/09/18 → …
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