\documentclass{midl}
\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\jmlrvolume{-- 274}
\editors{Accepted for publication at MIDL 2026}

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\title[ALO]{ALO: Addressing Class Imbalance in Radiology Report Generation through Anatomy-Level Oversampling}

\midlauthor{\Name{Lukas Buess\midljointauthortext{Equal Contribution}\nametag{$^{1}$}} \Email{Lukas.Buess@fau.de}\\
\Name{Robert Kurin\midlotherjointauthor\nametag{$^{1}$}} \Email{robert.kurin@studium.fau.de}\\
\Name{Adarsh {Bhandary Panambur}\nametag{$^{1}$}} \Email{Adarsh.Bhandary.Panambur@fau.de}\\
\Name{Tomas Arias-Vergara\nametag{$^{1}$}} \Email{Tomas.Arias@fau.de}\\
\Name{Andreas Maier\nametag{$^{1}$}} \Email{Andreas.Maier@fau.de}\\
\addr $^{1}$ Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
}

\begin{document}

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\begin{abstract}
Radiology report generation aims to connect visual understanding with clinical language, yet most methods rely on free-text supervision, which is highly variable and difficult to evaluate. Clinical datasets are also dominated by normal findings, causing models to underreport abnormalities. While recent works focus on architectural advances, we show that structured supervision and balanced sampling can yield substantial gains in clinical performance. We convert free-text reports into structured anatomy-level representations and introduce Anatomy-Level Oversampling (ALO), a data centered sampling strategy that balances normal and abnormal findings for each anatomical region. This structure provides consistent supervision and enables more informative evaluation. Across three public datasets, ALO improves sensitivity to pathological findings while remaining fully model agnostic. On internal validation, ALO increases F1-Score by 50\% and CRG by 5.8\%, and on external validation, it increases F1-Score by 45.1\% and CRG by 5\%. These results highlight the importance of structured data and balanced sampling for reliable report generation. Our code is publicly available\footnote{Code: \url{https://github.com/Kurin-FAU/ALO}}.
\end{abstract}

\begin{keywords}
Class imbalance, Structured report generation, Vision-language models.
\end{keywords}

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\input{sections_274/1_introduction}
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\input{sections_274/3_methods}
\input{sections_274/4_experimental_setup}
\input{sections_274/5_results_discussion}
\input{sections_274/6_conclusion}

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\midlacknowledgments{The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The hardware is funded by the German Research Foundation (DFG).}

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\appendix

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\input{appendix_274/2_extended_results}

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