X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/gpc2011.git/blobdiff_plain/57be57e49ce2eee18399dfa373e4aea6ad73ce61..70d6de89376491e1fefe8ef5a68591b9ce4b8773:/gpc2011.tex

diff --git a/gpc2011.tex b/gpc2011.tex
index 83a04b4..c9f08f8 100644
--- a/gpc2011.tex
+++ b/gpc2011.tex
@@ -47,7 +47,7 @@
 
 
 \author{Nabil Abdennhader\inst{1} \and Mohamed Ben Belgacem\inst{1} \and RaphaÃ«l Couturier\inst{2} \and
-  David Laiymani\inst{2} \and SÃ©bastien  MiquÃ©e\inst{2} \and Marko Niinimaki\inst{1} \and Marc Sauget\inst{2}}
+  David Laiymani\inst{2} \and SÃ©bastien  MiquÃ©e\inst{2} \and Marko Niinimaki\inst{1} \and Marc Sauget\inst{3}}
 
 \institute{
 University of Applied Sciences Western Switzerland, hepia Geneva,
@@ -60,7 +60,7 @@ Laboratoire d'Informatique de l'universit\'{e}
 \email{\{raphael.couturier,david.laiymani,sebastien.miquee\}@univ-fcomte.fr}
 \and
  FEMTO-ST, ENISYS/IRMA, F-25210 Montb\'{e}liard , FRANCE\\
-\email{marc.sauget@femto-st.fr}
+\email{marc.sauget@univ-fcomte.fr}
 }
 
 
@@ -95,13 +95,13 @@ techniques, using analytic methods, models and databases, are rapid
 but lack precision. Enhanced precision can be achieved by using
 calculation codes based, for example, on Monte Carlo methods. The main
 drawback of these methods is their computation times which can be
-rapidly huge. In \cite{} the authors proposed a novel approach, called
+rapidly huge. In \cite{NIMB2008} the authors proposed a novel approach, called
 Neurad, using neural networks. This approach is based on the
 collaboration of computation codes and multi-layer neural networks
 used as universal approximators. It provides a fast and accurate
 evaluation of radiation doses in any given environment for given
 irradiation parameters. As the learning step is often very time
-consuming, in \cite{} the authors proposed a parallel
+consuming, in \cite{AES2009} the authors proposed a parallel
 algorithm that enables to decompose the learning domain into
 subdomains. The decomposition has the advantage to significantly
 reduce the complexity of the target functions to approximate.
@@ -146,29 +146,27 @@ end in Section 6 by some concluding remarks and perspectives.
   \label{f_neurad}
 \end{figure}
 
-The \emph{Neurad}~\cite{Neurad} project presented in this paper takes
-place in a multi-disciplinary project, involving medical physicists
-and computer scientists whose goal is to enhance the treatment
-planning of cancerous tumors by external radiotherapy. In our previous
-works~\cite{RADIO09,ICANN10,NIMB2008}, we have proposed an original
-approach to solve scientific problems whose accurate modeling and/or
-analytical description are difficult. That method is based on the
-collaboration of computational codes and neural networks used as
-universal interpolator. Thanks to that method, the \emph{Neurad}
-software provides a fast and accurate evaluation of radiation doses in
-any given environment (possibly inhomogeneous) for given irradiation
-parameters. We have shown in a previous work (\cite{AES2009}) the
-interest to use a distributed algorithm for the neural network
-learning. We use a classical RPROP (DEFINITION)algorithm with a HPU
-topology to do the training of our neural network.
-
-Figure~\ref{f_neurad} presents the {\it{Neurad}} scheme. Three parts
-are clearly independent: the initial data production, the learning
-process and the dose deposit evaluation. The first step, the data
-production, is outside of the {\it{Neurad}} project. They are many
-solutions to obtain data about the radiotherapy treatments like the
-measure or the simulation. The only essential criterion is that the
-result must be obtained in an homogeneous environment. 
+The \emph{Neurad}~\cite{Neurad} project presented in this paper takes place in a
+multi-disciplinary project, involving medical physicists and computer scientists
+whose goal is to enhance the  treatment planning of cancerous tumors by external
+radiotherapy.   In our  previous works~\cite{RADIO09,ICANN10,NIMB2008},  we have
+proposed  an  original approach  to  solve  scientific  problems whose  accurate
+modeling and/or  analytical description are  difficult. That method is  based on
+the collaboration of  computational codes and neural networks  used as universal
+interpolator. Thanks to that method,  the \emph{Neurad} software provides a fast
+and accurate  evaluation of radiation  doses in any given  environment (possibly
+inhomogeneous) for  given irradiation  parameters. We have  shown in  a previous
+work (\cite{AES2009}) the interest to use a distributed algorithm for the neural
+network learning. We  use a classical RPROP~\footnote{Resilient backpropagation}
+algorithm with a  HPU~\footnote{High order processing units} topology  to do the
+training of our neural network.
+
+Figure~\ref{f_neurad} presents the {\it{Neurad}} scheme. Three parts are clearly
+independent:  the initial  data production,  the learning  process and  the dose
+deposit  evaluation. The  first step,  the data  production, is  outside  of the
+{\it{Neurad}}  project.  They  are  many  solutions to  obtain  data  about  the
+radiotherapy treatments like  the measure or the simulation.  The only essential
+criterion is that the result must be obtained in an homogeneous environment.
 
 % We have chosen to
 % use only a Monte Carlo simulation because this kind of tool is the
@@ -187,24 +185,22 @@ result must be obtained in an homogeneous environment.
 %   \label{f_tray}
 % \end{figure}
 
-The secondary stage of the {\it{Neurad}} project is the learning step
-and this is the most time consuming step. This step is performed
-off-line but it is important to reduce the time used for the learning
-process to keep a workable tool. Indeed, if the learning time is too
-huge (for the moment, this time could reach one week for a limited
-domain), this process should not be launched at any time, but only
-when a major modification occurs in the environment, like a change of
-context for instance. However, it is interesting to update the
-knowledge of the neural network, by using the learning process, when
-the domain evolves (evolution in material used for the prosthesis or
-evolution on the beam (size, shape or energy)). The learning time is
-related to the volume of data who could be very important in a real
-medical context.  A work has been done to reduce this learning time
-with the parallelization of the learning process by using a
-partitioning method of the global dataset. The goal of this method is
-to train many neural networks on sub-domains of the global
-dataset. After this training, the use of these neural networks all
-together allows to obtain a response for the global domain of study.
+The secondary stage  of the {\it{Neurad}} project is the  learning step and this
+is  the most time  consuming step.  This step  is performed  off-line but  it is
+important to  reduce the time used for  the learning process to  keep a workable
+tool. Indeed, if the learning time is  too huge (for the moment, this time could
+reach one week for a limited domain), this process should not be launched at any
+time,  but only  when a  major modification  occurs in  the environment,  like a
+change  of  context for  instance.  However, it  is  interesting  to update  the
+knowledge of the neural network, by  using the learning process, when the domain
+evolves (evolution in material used for  the prosthesis or evolution on the beam
+(size, shape or energy)). The learning time is related to the volume of data who
+could be  very important in  a real  medical context.  A  work has been  done to
+reduce this  learning time with the  parallelization of the  learning process by
+using a partitioning method of the global dataset. The goal of this method is to
+train  many neural networks  on sub-domains  of the  global dataset.  After this
+training,  the use  of these  neural networks  all together  allows to  obtain a
+response for the global domain of study.
 
 
 \begin{figure}[h]
@@ -215,22 +211,24 @@ together allows to obtain a response for the global domain of study.
   \label{fig:overlap}
 \end{figure}
 
-
-However, performing the learning on sub-domains constituting a
-partition of the initial domain is not satisfying according to the
-quality of the results. This comes from the fact that the accuracy of
-the approximation performed by a neural network is not constant over
-the learned domain. Thus, it is necessary to use an overlapping of
-the sub-domains. The overall principle is depicted in
-Figure~\ref{fig:overlap}. In this way, each sub-network has an
-exploitation domain smaller than its training domain and the
-differences observed at the borders are no longer relevant.
-Nonetheless, in order to preserve the performance of the parallel
-algorithm, it is important to carefully set the overlapping ratio
-$\alpha$. It must be large enough to avoid the border's errors, and
-as small as possible to limit the size increase of the data subsets
-(Qu'en est-il pour nos test ?).
-
+% j'ai relu mais pas vu le probleme 
+ 
+However, performing the learning on  sub-domains constituting a partition of the
+initial domain is  not satisfying according to the quality  of the results. This
+comes from the fact that the accuracy of the approximation performed by a neural
+network is not constant over the learned domain. Thus, it is necessary to use an
+overlapping  of   the  sub-domains.  The   overall  principle  is   depicted  in
+Figure~\ref{fig:overlap}.  In this  way,  each sub-network  has an  exploitation
+domain  smaller than its  training domain  and the  differences observed  at the
+borders  are  no  longer  relevant.   Nonetheless,  in  order  to  preserve  the
+performance  of the parallel  algorithm, it  is important  to carefully  set the
+overlapping  ratio $\alpha$.  It  must be  large  enough to  avoid the  border's
+errors,  and as  small  as  possible to  limit  the size  increase  of the  data
+subsets~\cite{AES2009}.
+
+%(Qu'en est-il pour nos test ?).
+% Ce paramÃ¨tre a deja Ã©tÃ© etudiÃ© dans un prÃ©cÃ©dent papier, il a donc choisi d'Ãªtre fixe
+% pour ces tests-ci.
 
 
 \section{The XtremWeb-CH environment}
@@ -412,7 +410,14 @@ Our future works include the testing of the application on a more
 large scale testbed. This implies, the choice of a data input set
 allowing a finer decomposition. Unfortunately, this choice of input
 data is not trivial and relies on a large number of parameters
-(demander ici des prÃ©cisions Ã  Marc).
+
+%(demander ici des prÃ©cisions Ã  Marc).
+% Si tu veux parler de l'ensembles des paramÃ¨tres que l'on peut utiliser pour caractÃ©riser les conditions d'irradiations
+% tu peux parler : 
+% - caracteristiques du faisceaux d'irradiation (beam size (de quelques mm Ã  plus de 40 cm),  energy, SSD (source surface distance), 
+% - caractÃ©ritiques de  la matiÃ¨re : density 
+
+
 
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