InforLorV4, Main, Exploration, bibRecord, 003083

Efficient Domain Decomposition for a Neural Network Learning Algorithm, Used for the Dose Evaluation in External Radiotherapy

Identifieur interne : 003083 ( Main/Exploration ); précédent : 003082; suivant : 003084

Efficient Domain Decomposition for a Neural Network Learning Algorithm, Used for the Dose Evaluation in External Radiotherapy

Auteurs : Marc Sauget [France] ; Rémy Laurent [France] ; Julien Henriet [France] ; Michel Salomon [France] ; Régine Gschwind [France] ; Sylvain Contassot-Vivier [France] ; Libor Makovicka [France] ; Charles Soussen [France]

Source :

Lecture Notes in Computer Science [ 0302-9743 ]

RBID : ISTEX:22AC94B35DD35EBDCE365E8E3A2D470E6362D1F0

Abstract

Abstract: The purpose of this work is to further study the relevance of accelerating the Monte Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit. Our parallel algorithm consists in a regular decomposition of the initial learning dataset (also called learning domain) in as much subsets as available processors. However, the initial learning set presents heterogeneous signal complexities and consequently, the learning times of regular subsets are very different. This paper presents an efficient learning domain decomposition which balances the signal complexities across the processors. As will be shown, the resulting irregular decomposition allows for important gains in learning time of the global network.

Url:

DOI: 10.1007/978-3-642-15819-3_34

Affiliations:

Le document en format XML

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Efficient Domain Decomposition for a Neural Network Learning Algorithm, Used for the Dose Evaluation in External Radiotherapy

Efficient Domain Decomposition for a Neural Network Learning Algorithm, Used for the Dose Evaluation in External Radiotherapy

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Abstract

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