MRI Gastric Images Processing using a Multiobjective Fly Algorithm

Shatha Al-Maliki; Evelyne Lutton; Francois Boue; Franck Vidal

MRI Gastric Images Processing using a Multiobjective Fly Algorithm

Shatha Al-Maliki, Evelyne Lutton, Francois Boue, Franck Vidal

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Abstract

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.

Original language	English
Publication status	Published - 8 Sept 2018
Event	Evolutionary Machine Learning - University of Coimbra, Coimbra, Portugal Duration: 8 Sept 2018 → … http://ppsn2018.dei.uc.pt/index.php/workshops/

Workshop

Workshop	Evolutionary Machine Learning
Abbreviated title	EvoML
Country/Territory	Portugal
City	Coimbra
Period	8/09/18 → …
Internet address	http://ppsn2018.dei.uc.pt/index.php/workshops/

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PPSN___paper__Copy_Accepted author manuscript, 3.68 MBLicence: CC BY

Cite this

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title = "MRI Gastric Images Processing using a Multiobjective Fly Algorithm",

abstract = "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.",

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AU - Lutton, Evelyne

AU - Boue, Francois

AU - Vidal, Franck

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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 -

MRI Gastric Images Processing using a Multiobjective Fly Algorithm

Abstract

Workshop

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