A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation

TL;DR

This paper introduces a dual-stage, disease-aware framework for chest X-ray report generation that enhances clinical accuracy by integrating disease-specific representations and visual features, outperforming existing methods.

Contribution

The proposed framework uniquely combines disease-aware semantic tokens, attention fusion, and similarity retrieval to improve report quality and clinical relevance in medical image analysis.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Significantly improves clinical accuracy of generated reports.

Enhances linguistic quality and contextual relevance.

Abstract

Radiology report generation from chest X-rays is an important task in artificial intelligence with the potential to greatly reduce radiologists' workload and shorten patient wait times. Despite recent advances, existing approaches often lack sufficient disease-awareness in visual representations and adequate vision-language alignment to meet the specialized requirements of medical image analysis. As a result, these models usually overlook critical pathological features on chest X-rays and struggle to generate clinically accurate reports. To address these limitations, we propose a novel dual-stage disease-aware framework for chest X-ray report generation. In Stage~1, our model learns Disease-Aware Semantic Tokens (DASTs) corresponding to specific pathology categories through cross-attention mechanisms and multi-label classification, while simultaneously aligning vision and language representations via contrastive learning. In Stage~2, we introduce a Disease-Visual Attention Fusion (DVAF) module to integrate disease-aware representations with visual features, along with a Dual-Modal Similarity Retrieval (DMSR) mechanism that combines visual and disease-specific similarities to retrieve relevant exemplars, providing contextual guidance during report generation. Extensive experiments on benchmark datasets (i.e., CheXpert Plus, IU X-ray, and MIMIC-CXR) demonstrate that our disease-aware framework achieves state-of-the-art performance in chest X-ray report generation, with significant improvements in clinical accuracy and linguistic quality.