TY - CONF

T1 - MRI Gastric Images Processing using a Multiobjective Fly Algorithm

AU - Al-Maliki, Shatha

AU - Lutton, Evelyne

AU - Boue, Francois

AU - Vidal, Franck

PY - 2018/9/8

Y1 - 2018/9/8

N2 - 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.

AB - 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.

M3 - Paper

T2 - Evolutionary Machine Learning

Y2 - 8 September 2018

ER -