\documentclass{midl} % Include author names
\usepackage{xcolor}
% Toggle for revision coloring
\newif\ifrevisedcolor
\revisedcolortrue % Set to \revisedcolorfalse for camera-ready version

\newcommand{\rev}[1]{%
  \ifrevisedcolor
    \textcolor{black}{#1}%
  \else
    #1%
  \fi
}
% \usepackage{graphicx} % Required for inserting images
\usepackage{algorithm, algorithmic}
\usepackage{booktabs}
\usepackage{multirow}
\usepackage{adjustbox}
% The following packages will be automatically loaded:
% jmlr, amsmath, amssymb, natbib, graphicx, url, algorithm2e
% ifoddpage, relsize and probably more
% make sure they are installed with your latex distribution

\usepackage{mwe} % to get dummy images
% \jmlrvolume{-- Under Review}
% \jmlryear{2025}
% \jmlrworkshop{Full Paper -- MIDL 2025 submission}
% \editors{Under Review for MIDL 2025}

\jmlryear{2025}
\jmlrworkshop{Full Paper -- MIDL 2025}
\jmlrvolume{-- nnn}
\editors{Accepted for publication at MIDL 2025}

\title[Adversarial Perturbations for Confidence Prediction]{Adversarial Perturbations Improve Generalization of Confidence Prediction in Medical Image Segmentation}

 % Use \Name{Author Name} to specify the name.
 % If the surname contains spaces, enclose the surname
 % in braces, e.g. \Name{John {Smith Jones}} similarly
 % if the name has a "von" part, e.g \Name{Jane {de Winter}}.
 % If the first letter in the forenames is a diacritic
 % enclose the diacritic in braces, e.g. \Name{{\'E}louise Smith}

%Two authors with the same address
\midlauthor{\Name{Jonathan Lennartz} \Email{lennartz@cs.uni-bonn.de}\\
 \Name{Thomas Schultz} \Email{schultz@cs.uni-bonn.de}\\
 \addr University of Bonn, Germany\\
\addr Lamarr Institute for Machine Learning and Artificial Intelligence, Germany}

 % Three or more authors with the same address:
 % \midlauthor{\Name{Author Name1} \Email{an1@sample.edu}\\
 %  \Name{Author Name2} \Email{an2@sample.edu}\\
 %  \Name{Author Name3} \Email{an3@sample.edu}\\
 %  \addr Address}


% Authors with different addresses:
% \midlauthor{\Name{Author Name1} \Email{abc@sample.edu}\\
% \addr Address 1
% \AND
% \Name{Author Name2} \Email{xyz@sample.edu}\\
% \addr Address 2
% }

%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
%\midlauthor{\Name{Author Name1\midljointauthortext{Contributed equally}\nametag{$^{1,2}$}} \orcid{1111-2222-3333-4444} \Email{abc@sample.edu}\\
%\addr $^{1}$ Address 1 \\
%\addr $^{2}$ Address 2 \AND
%\Name{Author Name2\midlotherjointauthor\nametag{$^{1}$}} \Email{xyz@sample.edu}\\
%\Name{Author Name3\nametag{$^{2}$}} \Email{alphabeta@example.edu}\\
%\Name{Author Name4\midljointauthortext{Contributed equally}\nametag{$^{3}$}} \Email{uvw@foo.ac.uk}\\
%\addr $^{3}$ Address 3 \AND
%\Name{Author Name5\midlotherjointauthor\nametag{$^{4}$}} \Email{fgh@bar.com}\\
%\addr $^{4}$ Address 4
%}

\begin{document}

\maketitle

\begin{abstract}
  Trustworthy methods for medical image segmentation should come with a reliable mechanism to estimate the quality of their results. Training a separate component for confidence prediction is relatively fast, and can easily be adapted to different quality metrics. However, the resulting estimates are usually not sufficiently reliable under domain shifts, for example when images are taken with different devices. We introduce a novel adversarial strategy for training confidence predictors for the widely used U-Net architecture that greatly improves such generalization. It is based on creating adversarial image perturbations, aimed at substantially decreasing segmentation quality, via the gradients of the confidence predictor, leading to images outside of the original training distribution. We observe that these perturbations initially have little effect on segmentation quality. However, including them in the training gradually improves the confidence predictor's understanding of what actually affects segmentation quality when moving outside of the training distribution. On two different medical image segmentation tasks, we demonstrate that this strategy substantially improves estimates of volumetric and surface Dice on out-of-distribution images.
\end{abstract}

\begin{keywords}
  \rev{Failure Detection}, Domain Shift.
\end{keywords}

%\newpage
\input{sections/introduction.tex}
\input{sections/related_work.tex}
\input{sections/methodology.tex}
\input{sections/experiments.tex}
\input{sections/discussion.tex}

\clearpage  % Acknowledgements, references, and appendix do not count toward the page limit (if any)
% Acknowledgments---Will not appear in anonymized version
%\midlacknowledgments{We thank a bunch of people.}


\bibliography{midl25_190}

\newpage
\appendix
\input{sections/appendix.tex}

\end{document}
