Semantic-Aware Chest X-Ray Report Generation With Domain-Specific Lexicon and Diversity-Controlled Retrieval
MCML Authors
Nassir Navab
Prof. Dr.
Principal Investigator
Abstract
Nassir Navab
Prof. Dr.
Principal Investigator
Abstract
Image-to-text radiology report generation aims to produce comprehensive diagnostic reports by leveraging both X-ray images and historical textual data. Existing retrieval-based methods focus on maximizing similarity scores, leading to redundant content and limited diversity in generated reports. Additionally, they lack sensitivity to medical domain-specific information, failing to emphasize critical anatomical structures and disease characteristics essential for accurate diagnosis. To address these limitations, we propose a novel retrieval-augmented framework that integrates exemplar radiology reports with X-ray images to enhance report generation. First, we introduce a diversity-controlled retrieval strategy to improve information diversity and reduce redundancy, ensuring broader clinical knowledge coverage. Second, we develop a comprehensive medical lexicon covering chest anatomy, diseases, radiological descriptors, treatments, and related concepts. This lexicon is integrated into a weighted cross-entropy loss function to improve the model’s sensitivity to critical medical terms. Third, we introduce a sentence-level semantic loss to enhance clinical semantic accuracy. Evaluated on the MIMIC-CXR dataset,our method achieves superior performance on clinical consistency metrics and competitive results on linguistic quality metrics, demonstrating its effectiveness in enhancing report accuracy and clinical relevance.
inproceedings
MICCAI 2025
28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025.
Authors
B. Zhang • C. Jia • S. Liu • H. Schunkert • N. NavabLinks
DOI GitHubResearch Area
BibTeXKey: ZJL+25