\documentclass{midl}

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\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\jmlrvolume{-- nnn}
\editors{Accepted for publication at MIDL 2026}

\title[SemiSynCXR]{Semi-Synthetic Localization Datasets for Radiological Findings on Chest X-Rays}

\midlauthor{\Name{Andrea Posada\nametag{$^{1,2}$}} \orcid{0009-0005-3021-5525} 
\Email{andrea.posada-cardenas@tum.de}\\
\Name{Johannes Brandt\nametag{$^{1}$}} \Email{johannes.brandt@tum.de}\\
\Name{Friederike Jungmann\nametag{$^{1,3}$}} \Email{friederike.jungmann@tum.de}\\
\Name{Maria Posada\nametag{$^{4}$}} \Email{maria.posadac@udea.edu.co}\\
\Name{Daniel Rueckert\nametag{$^{1,2,5}$}} \Email{daniel.rueckert@tum.de}\\
\Name{Martin J. Menten\nametag{$^{1,2}$}} \Email{martin.menten@tum.de}\\
\Name{Felix Meissen\nametag{$^{1}$}} \Email{felix.meissen@tum.de}\\
\Name{Philip M\"uller\nametag{$^{1}$}} \Email{philip.j.mueller@tum.de}\AND
\addr $^{1}$ Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany\\
\addr $^{2}$ Munich Center for Machine Learning (MCML), Munich, Germany\\
\addr $^{3}$ Department of Diagnostic and Interventional Radiology, TUM University Hospital, Technical University of Munich (TUM), Munich, Germany\\
\addr $^{4}$ Independent Researcher, Medellin, Colombia\\
\addr $^{5}$ Department of Computing, Imperial College London, UK
}

\begin{document}

\maketitle

\begin{abstract}
While large datasets for chest X-ray (CXR) finding classification are widely available, datasets for finding localization are scarce. Curating these localization datasets is costly and time-intensive, requiring manual annotation by medical experts, which often results in them being small and limited in scope. To overcome this, we introduce \emph{SemiSynCXR}, a framework designed to automatically generate semi-synthetic localization datasets. \emph{SemiSynCXR} operates by inpainting specific radiological findings into real, healthy CXRs at anatomically plausible locations, which allows for the output of both the edited image and the ground-truth bounding box for each finding. \emph{SemiSynCXR}-generated CXRs effectively augment existing localization datasets, yielding relative mAP$_{10:70}$ gains of up to 11\% on in-domain and 21\% on out-of-domain data, thereby mitigating data scarcity and improving generalization. Comprehensive quantitative and qualitative evaluations show that our framework achieves an overall AUROC of 0.78 and mAP$_{10:70}$ of 0.45, comparable to fully synthetic benchmarks. These results confirm that the generated findings are realistic and accurately localized, establishing \emph{SemiSynCXR} as a practical solution for the generation of CXR finding localization datasets. Code available at the \href{https://github.com/anpoc/SemiSynCXR}{SemiSynCXR GitHub Repository}.
\end{abstract}

\begin{keywords}
Semi-synthetic CXRs, Inpainting, Diffusion models
\end{keywords}

\input{MIDLLatexTemplate-master/sections/1_intro}
\input{MIDLLatexTemplate-master/sections/2_relatedwork}
\input{MIDLLatexTemplate-master/sections/3_methodology}
\input{MIDLLatexTemplate-master/sections/4_results}
\input{MIDLLatexTemplate-master/sections/5_conclusions}

\clearpage
\midlacknowledgments{Andrea Posada and Martin J. Menten are funded by the German Research Foundation under project 532139938.}
\bibliography{midl26_244}

\clearpage
\appendix
\input{MIDLLatexTemplate-master/sections/90_results}
\clearpage
\input{MIDLLatexTemplate-master/sections/91_datasets}
\clearpage
\input{MIDLLatexTemplate-master/sections/92_method}
\clearpage
\input{MIDLLatexTemplate-master/sections/93_configs}

\end{document}
