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

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

106 Wedi eu Llwytho i Lawr (Pure)

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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 wreiddiol	Saesneg
Statws	Cyhoeddwyd - 8 Medi 2018
Digwyddiad	Evolutionary Machine Learning - University of Coimbra, Coimbra, Portiwgal Hyd: 8 Medi 2018 → … http://ppsn2018.dei.uc.pt/index.php/workshops/

Gweithdy

Gweithdy	Evolutionary Machine Learning
Teitl cryno	EvoML
Gwlad/Tiriogaeth	Portiwgal
Dinas	Coimbra
Cyfnod	8/09/18 → …
Cyfeiriad rhyngrwyd	http://ppsn2018.dei.uc.pt/index.php/workshops/

Mynediad at Ddogfen

PPSN___paper__Copy_Llawysgrif awdur wedi’i dderbyn, 3.68 MBTrwydded: CC BY

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@conference{8a1713ef354b48deb0f4f08b515ffc4e,

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. ",

author = "Shatha Al-Maliki and Evelyne Lutton and Francois Boue and Franck Vidal",

year = "2018",

month = sep,

day = "8",

language = "English",

note = "Evolutionary Machine Learning, EvoML ; Conference date: 08-09-2018",

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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 -

MRI Gastric Images Processing using a Multiobjective Fly Algorithm

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