X-Gen: Enhancing Radiology Report Generation via LLM-Driven Data Augmentation and Decoupled Training

Abstract

The scarcity and limited accessibility of medical data significantly challenge deep learning applications in medical AI. Radiology report generation (RRG), a key medical AI research area, could greatly improve computer-aided diagnosis through automated X-ray image interpretation. However, obtaining paired <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex>-ray images and reports is labor-intensive and restricted by strict regulations. Large language models (LLMs), such as GPT-4, provide a promising alternative by enabling cost-effective text data augmentation and report rewriting in varied styles. We rigorously assess augmented data's clinical accuracy and stylistic similarity to radiologist-authored reports through expert evaluations. Interestingly, augmented data enhances RRG model performance, yet performance declines when augmented data surpasses original data volume due to style distribution shifts. To mitigate this, we propose integrating a conditional variational autoencoder (cVAE) into the RRG model to separate medical semantics from writing styles during training, enabling better handling of augmented data's distribution shift. Our proposed method, X-Gen, combines data augmentation with decoupled training. Tested on two public Chest X-ray datasets and a private abdomen X-ray dataset, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex>-Gen significantly improves the performance of baseline models, showcasing its effectiveness and versatility in X-ray report generation.

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