Anatomy-guided radiology report generation with pathology-aware regional prompts
File(s)
Author(s)
Type
Journal Article
Abstract
Goal: Radiology report generation holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy remains challenging, as radiographs often feature intricate structures and subtle pathologies. Methods: To address these challenges, this work introduces an innovative approach that explicitly integrates anatomical and pathological information into report decoding by leveraging pathology-aware regional prompts. Specifically, we develop an anatomical region detector that extracts structured visual features from distinct anatomical areas, coupled with a novel multi-label pathology detector that identifies global abnormalities. Results: Our model demonstrates superior report generation performance in natural language generation and clinical efficacy, surpassing previous state-of-the-art methods. It achieved scores of 0.394 in BLEU-1, 0.302 in ROUGE-L, and 0.470 in F1, reflecting substantial improvements in both linguistic fluency and medical accuracy. Formal expert evaluations further affirmed the model's potential to elevate radiology practice. Conclusion: By integrating anatomical and pathological insights to emulate radiologists' workflow, our model achieves superior accuracy and clinical coherence of radiology reporting. It offers remarkable promise to support clinical decision-making and transform patient management.
Date Issued
2026-04-23
Date Acceptance
2026-04-18
Citation
IEEE Open Journal of Engineering in Medicine and Biology, 2026, 7, pp.165-171
ISSN
2644-1276
Publisher
IEEE
Start Page
165
End Page
171
Journal / Book Title
IEEE Open Journal of Engineering in Medicine and Biology
Volume
7
Copyright Statement
© 2026 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
10.1109/OJEMB.2026.3687122
Publication Status
Published
Date Publish Online
2026-04-23