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Manual examination of chest x-rays is a time consuming process that involves significant effort by expert radiologists. Recent work attempts to alleviate this problem by developing learning-based automated chest x-ray analysis systems that map images to multi-label diagnoses using deep neural net- works. These methods are often treated as black boxes, or they output attention maps but don’t explain why the attended areas are important. Given data consisting of a frontal-view x-ray, a set of natural language findings, and one or more diagnostic impressions, we propose a deep neural network model that during training simultaneously 1) constructs a topic model which clusters key terms from the findings into meaningful groups, 2) predicts the presence of each topic for a given input image based on learned visual features, and 3) uses an image’s predicted topic encoding as features to predict one or more diagnoses. Since the net learns the topic model jointly with the classifier, it gives us a powerful tool for understanding which semantic concepts the net might be ex- ploiting when making diagnoses, and since we constrain the net to predict topics based on expert-annotated reports, the net automatically encodes some higher-level expert knowledge about how to make diagnoses.
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A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation
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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- Award ID(s):
- 2145640
- PAR ID:
- 10674686
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 40
- Issue:
- 40
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 33953 to 33961
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1609/aaai.v40i40.40688
