Automated Structured Radiology Report Generation
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Automated radiology report generation from chest X-ray (CXR) images has the
potential to improve clinical efficiency and reduce radiologists' workload.
However, most datasets, including the publicly available MIMIC-CXR and CheXpert
Plus, consist entirely of free-form reports, which are inherently variable and
unstructured. This variability poses challenges for both generation and
evaluation: existing models struggle to produce consistent, clinically
meaningful reports, and standard evaluation metrics fail to capture the nuances
of radiological interpretation. To address this, we introduce Structured
Radiology Report Generation (SRRG), a new task that reformulates free-text
radiology reports into a standardized format, ensuring clarity, consistency,
and structured clinical reporting. We create a novel dataset by restructuring
reports using large language models (LLMs) following strict structured
reporting desiderata. Additionally, we introduce SRR-BERT, a fine-grained
disease classification model trained on 55 labels, enabling more precise and
clinically informed evaluation of structured reports. To assess report quality,
we propose F1-SRR-BERT, a metric that leverages SRR-BERT's hierarchical disease
taxonomy to bridge the gap between free-text variability and structured
clinical reporting. We validate our dataset through a reader study conducted by
five board-certified radiologists and extensive benchmarking experiments.
