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

• URL: http://arxiv.org/abs/2511.12259v1
• Date: Sat, 15 Nov 2025 15:31:51 GMT
• Title: A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation
• Authors: Puzhen Wu, Hexin Dong, Yi Lin, Yihao Ding, Yifan Peng,
• Abstract summary: We propose a novel dual-stage disease-aware framework for chest X-ray report generation.<n>In Stage1, our model learns Disease-Aware Semantic Tokens (DASTs) corresponding to specific pathology categories.<n>In Stage2, we introduce a Disease-Visual Attention Fusion module to integrate disease-aware representations with visual features.
• Score: 15.331803613974365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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