% Template file for an a0 portrait poster.
% Written by Graeme, 2001-03 based on his SOC poster.
%
% See discussion and documentation at
% <http://www.astro.gla.ac.uk/users/norman/docs/posters/>
%
% $Id: pais.tex,v 1.12 2006/08/08 06:34:34 perry Exp $



% We switch to portrait mode. This works as advertised.
\documentclass[a0,portrait]{a0poster}
% You might find the 'draft' option to a0 poster useful if you have
% lots of graphics, because they can take some time to process and
% display. (\documentclass[a0,draft]{a0poster})

% Switch off page numbers on a poster, obviously, and section numbers too.
\pagestyle{empty}
\setcounter{secnumdepth}{0}

% The textpos package is necessary to position textblocks at arbitrary
% places on the page.
\usepackage[absolute]{textpos}          % without boxes
%\usepackage[absolute,showboxes]{textpos}   % with boxes

% Graphics to include graphics. Times is nice on posters, but you
% might want to switch it off and go for CMR fonts.
\usepackage{graphics,wrapfig,times,epsfig}

\usepackage{edposter}   % for border definitions
\usepackage{latexsym}   % for diamond operator
\usepackage{fancybox}
\usepackage{pifont}
\usepackage{picins}
\usepackage{psfrag}

\newcommand{\textphv}[1]{{\fontfamily{phv}\small\selectfont #1}}

% These colours are tried and tested for titles and headers. Don't
% over use colour!
\usepackage{color}
\definecolor{DarkBlue}{rgb}{0.1,0.1,0.5}
\definecolor{Red}{rgb}{0.9,0.0,0.1}
\definecolor{LightYellow}{rgb}{1,1,0.6}


% see documentation for a0poster class for the size options here
\let\Textsize\normalsize
\def\Head#1{\noindent\hbox to \hsize{\hfil{\LARGE\color{DarkBlue} #1}}\bigskip}
\def\LHead#1{\noindent{\LARGE\color{DarkBlue} #1}\smallskip}
\def\Subhead#1{\noindent{\large\color{DarkBlue} #1}}
\def\Title#1{\noindent{\VeryHuge\color{Red} #1}}

% Set up the grid
%
% Note that [40mm,40mm] is the margin round the edge of the page --
% it is _not_ the grid size. That is always defined as
% PAGE_WIDTH/HGRID and PAGE_HEIGHT/VGRID. In this case we use
% 15 x 25. This gives us a wide central column for text (7 grid
% spacings) and two narrow columns (3 each) at each side for
% pictures, separated by 1 grid spacing.
%
% Note however that textblocks can be positioned fractionally as well,
% so really any convenient grid size can be used.
%
\TPGrid[40mm,40mm]{15}{25}  % 3 - 1 - 7 - 1 - 3 Columns

% Mess with these as you like
\parindent=0pt
%\parindent=1cm
\parskip=0.5\baselineskip

% abbreviations
\newcommand{\ddd}{\,\mathrm{d}}

\begin{document}
\sloppy

\input{head}

% Background + Problem Statement + Aims
\begin{textblock}{4.3}(0,5)
\input{background}
\end{textblock}


% -- bottom left: breast

\begin{textblock}{4.4}(-0.1,12.4)
\fcolorbox{Black}{Yellow}{\textcolor{Red}{
\begin{minipage}[t]{4.4\TPHorizModule}
\centerline{\VeryHuge{The Breast}}
\end{minipage}}}
\end{textblock}

\input{breast.tex}
\begin{textblock}{4.4}(-0.1,13)
\fcolorbox{Black}{LightYellow}{\begin{minipage}[t][8\TPVertModule]{4.4\TPHorizModule}~\end{minipage}}
\end{textblock}

\begin{textblock}{4.4}(-0.105,12.39)
\fbox{\begin{minipage}[t][8.605\TPVertModule]{4.41\TPHorizModule}~\end{minipage}}
\end{textblock}



\input{mammography.tex}

\begin{textblock}{10.5}(4.5,5)
\fcolorbox{Black}{Yellow}{\textcolor{Red}{
\begin{minipage}[t]{10.5\TPHorizModule}
\centerline{\VeryHuge{Mammography}}
\end{minipage}}}
\end{textblock}

\begin{textblock}{10.5}(4.5,5.8)
\fcolorbox{Black}{LightYellow}{\begin{minipage}[t][5.9\TPVertModule]{10.5\TPHorizModule}~\end{minipage}}
\end{textblock}

\begin{textblock}{10.5}(4.49,4.99)
\fbox{\begin{minipage}[t][6.71\TPVertModule]{10.51\TPHorizModule}~\end{minipage}}
\end{textblock}
% -----

\begin{textblock}{10.5}(4.5,12)
\LHead{Methodology}

Learning Bayesian networks from data involves learning the \emph{network topology} and learning the \emph{parameters}. As the possible network structures are exponentially large, we restrict them to Bayesian classifiers. Variables will represent continuous images features, which need to be reduced~~ using for example~ feature~ selection~ and principal~ component~ analysis. Most~ effort will be put though in using expert 
knowledge to train a series of simple classifiers on separate subsets (representing different signs, views, or distinct aspects like lesion boundary and effect of a lesion on its surroundings). The output of these simple classifiers can then be incorporated as input to another Bayesian network. \\
\begin{minipage}[t][5\TPVertModule]{4.7\TPHorizModule}~\end{minipage}
\begin{minipage}[t]{5.8\TPHorizModule}
%~\\
Classifier training will be started using existing algorithms and $N$-fold cross-validation. A preliminary investigation already has shown adequate support for continuous variables (possibly after data normalisation techniques). Preliminary results shows that a Naive Bayesian classifier can give comparable results with a Support Vector Machine, which currently gives the best classifier performance. \\

\begin{tabular}{lp{5.5\TPHorizModule}}
$[1]$ & M. Samulski, N. Karssemeijer, P. Lucas, and P. Groot.
Classification of mammographic masses using support vector machines and Bayesian networks. In \emph{SPIE Medical Imaging}, 2007.
\end{tabular}
\end{minipage}
\end{textblock}

%\begin{textblock}{2}(8.73,15.85)
%\begin{minipage}[t]{2\TPHorizModule}
%The classifiers will be evaluated using Receiver Operator Characteristic (ROC) or the Free-response Receiver Operator Characteristic (FROC), which are widely accepted evaluation methodology standards in Radiology.
%\end{minipage}
%\end{textblock}

\begin{textblock}{4.5}(4.5,14)
\centerline{\includegraphics[width=4.5\TPHorizModule]{images/4classifiers.epsi}}
Bayesian Network classifiers based on different models for representing dependencies between evidence variables $E_i$ for a class $C$. The Naive Bayesian classifier (NB, top left), Tree-augmented Naive Bayesian classifier (TAN, top right), Forrest-augmented Naive Bayesian classifier (FAN, bottom left), and Bayesian Network augmented Naive Bayesian classifier (BAN, bottom right).\\
~\\
\rule{4.5\TPHorizModule}{0.5mm}
~\\~\\
In the right picture, on the left the certainty of a tumour being benign relative to malign is given. On the right the corresponding ROC curves are shown. The area under the ROC curve, the value $A_z$, is used for measuring the performance. The better we are to distinguish malign from benign cases, the higher $A_z$ will be for the corresponding ROC curve.
\end{textblock}

\begin{textblock}{5.8}(9.2,16.3)
\begin{center}
\includegraphics[width=2.7\TPHorizModule]{images/roc1.epsi}
\hfill
\includegraphics[width=3\TPHorizModule]{images/roc2.epsi}
\end{center}
\end{textblock}




%


\begin{textblock}{15}(0,22)
\begin{center}
\Large{The B-Screen project is part of the} \\
\VeryHuge{The Nijmegen Health Care Computing Initiative} \\
\Large{~\\funded by NWO, hefboom program, Pr. Nr. 641.000.403\\ 
Perry Groot, Marko van Eekelen, Peter Lucas}
\end{center}
\end{textblock}

\begin{textblock}{15}(-0.1,22)
\fbox{\colorbox{Yellow}{\begin{minipage}[t][2.5\TPVertModule]{15\TPHorizModule}
~
\end{minipage}}}
\end{textblock}

\end{document}
