\section{Related Work}

\noindent\textbf{Radiology Report Generation}:
Radiology report generation is a central task in medical multimodal learning, where models aim to translate imaging data into clinically meaningful text. Most existing systems frame the task as free-text report generation, producing full narrative reports directly from images \citep{pellegrini2025radialog, hyland2023maira}. This direction has been enabled by large paired datasets \citep{johnson2019mimic, zhang2025rexgradient} and, more recently, CT-focused resources such as CT-RATE \citep{hamamci2026generalist} and Merlin \citep{blankemeier2026merlin}. Multimodal foundation models and large-scale pretraining \citep{agrawal2025pillar, buess2025speechct, liu2025t3d} have further advanced the field by providing stronger visual encoders and more capable language models. Agentic systems \citep{mao2025ct} extend this direction by using large language models (LLMs) to refine or critique reports, improving coherence and clinical correctness.

To address limitations inherent in free-text supervision, several studies have introduced structured formulations. \citet{delbrouck2025automated} and \citet{moll2025structuring} propose the structured radiology report generation task, converting free-text reports into standardized templates to reduce variability and enable clearer evaluation. \citet{keicher2024flexr} present FlexR, a few-shot classification framework operating on standardized report formats that uses language embeddings for structured prediction with minimal annotation. These efforts demonstrate the value of structured supervision, yet most state-of-the-art systems still rely on free-text, patient-level training without anatomical organization.

\noindent\textbf{Class Imbalance in Medical AI}:
Class imbalance is a well-known challenge in medical image analysis, where normal conditions greatly outnumber abnormal ones. This skew can bias models toward predicting healthy cases and reduce sensitivity to clinically important pathologies \citep{salmi2024handling}. Common mitigation strategies include weighted losses, sampling adjustments, and data augmentation \citep{chawla2002smote, liu2025anatomy, yun2011effective}, though these are typically used for classification or segmentation tasks. In radiology report generation, class imbalance is harder to address because LLMs are trained using token-wise cross-entropy over patient-level free-text rather than explicit class-level supervision. As a result, standard imbalance-handling techniques such as weighted or focal losses are not directly applicable. Moreover, na\"ive patient-level oversampling would also amplify normal findings from unrelated anatomical regions, reinforcing the normality bias. To our knowledge, anatomy-level imbalance has received little attention in report generation. Our approach addresses this gap by rebalancing abnormal content at the anatomy level.
