A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Generating radiology reports from CT scans remains a complex task due to the
nuanced nature of medical imaging and the variability in clinical
documentation. In this study, we propose a two-stage framework for generating
renal radiology reports from 2D CT slices. First, we extract structured
abnormality features using a multi-task learning model trained to identify
lesion attributes such as location, size, enhancement, and attenuation. These
extracted features are subsequently combined with the corresponding CT image
and fed into a fine-tuned vision-language model to generate natural language
report sentences aligned with clinical findings. We conduct experiments on a
curated dataset of renal CT studies with manually annotated
sentence-slice-feature triplets and evaluate performance using both
classification metrics and natural language generation metrics. Our results
demonstrate that the proposed model outperforms random baselines across all
abnormality types, and the generated reports capture key clinical content with
reasonable textual accuracy. This exploratory work highlights the feasibility
of modular, feature-informed report generation for renal imaging. Future
efforts will focus on extending this pipeline to 3D CT volumes and further
improving clinical fidelity in multimodal medical AI systems.
