\documentclass{midl} % Include author names
% \documentclass[anon]{midl} % Anonymized submission

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% \jmlrvolume{-- Under Review}
% \jmlryear{2024}
% \jmlrworkshop{Full Paper -- MIDL 2024 submission}
% \editors{Under Review for MIDL 2024}

\jmlryear{2024}\jmlrworkshop{Full Paper -- MIDL 2024}\jmlrvolume{-- nnn}\editors{Accepted for publication at MIDL 2024}

\title[Hyperparameter-Free Image Synthesis and Sharing for Segmentation]{Hyperparameter-Free Medical Image Synthesis for\\Sharing Data and Improving Site-Specific Segmentation}

 % Use \Name{Author Name} to specify the name.
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%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
\midlauthor{\Name{Alexander Chebykin\nametag{$^{1}$}} \Email{a.chebykin@cwi.nl}\\
\Name{Peter A. N. Bosman\nametag{$^{1,2}$}} \Email{peter.bosman@cwi.nl} \\
\Name{Tanja Alderliesten\nametag{$^{3}$}} \Email{t.alderliesten@lumc.nl}\\
\\
\addr $^{1}$ Centrum Wiskunde \& Informatica, Amsterdam, The Netherlands \\
\addr $^{2}$ Delft University of Technology, Delft, The Netherlands \\
\addr $^{3}$ Leiden University Medical Center, Leiden, The Netherlands
}

\begin{document}
\maketitle
% \vspace{-5pt}
\begin{abstract}

Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for medical image synthesis must be manually adjusted when they are applied to unseen data. To remove this manual burden, we introduce a \textbf{Hy}perparameter-\textbf{Free} distributed learning method for automatic medical image \textbf{S}ynthesis, \textbf{S}haring, and \textbf{S}egmentation called \textbf{HyFree-S3}. For three diverse segmentation settings (pelvic MRIs, lung X-rays, polyp photos), the use of HyFree-S3 results in improved performance over training only with site-specific data (in the majority of cases). The hyperparameter-free nature of the method should make data synthesis and sharing easier, potentially leading to an increase in the quantity of available data and consequently the quality of the models trained that may ultimately be applied in the clinic. Our code is available at \url{https://github.com/AwesomeLemon/HyFree-S3}.

\end{abstract}

\begin{keywords}
Synthetic data, Distributed Learning, AutoML, Segmentation.
\end{keywords}

\input{1_intro}

\input{2_related}

\input{3_method}

\input{4_experiments}

\input{5_discussion}

% Acknowledgments---Will not appear in anonymized version
\midlacknowledgments{This work is part of the research project DAEDALUS which is funded via the Open Technology Programme of the Dutch Research Council (NWO), project number 18373; part of the funding is provided by Elekta and ORTEC LogiqCare. We acknowledge the use of ChatGPT for grammar assistance in the preparation of this manuscript.}


\bibliography{midl24_294}

\clearpage
\input{7_appendix}
\end{document}