Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
This work addresses the challenge of reducing radiologists' workload in summarizing diagnostic reports, though it is incremental as it builds on existing methods by incorporating structural insights.
The authors tackled the problem of automatically generating chest X-ray reports by exploiting the structural information between sections (Findings and Impression) and within sections (imbalanced normality/abnormality distribution), achieving state-of-the-art performance on two datasets as measured by various evaluation metrics.
Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologists for summarizing descriptive findings and conclusive impressions. The complex structures between and within sections of the reports pose a great challenge to the automatic report generation. Specifically, the section Impression is a diagnostic summarization over the section Findings; and the appearance of normality dominates each section over that of abnormality. Existing studies rarely explore and consider this fundamental structure information. In this work, we propose a novel framework that exploits the structure information between and within report sections for generating CXR imaging reports. First, we propose a two-stage strategy that explicitly models the relationship between Findings and Impression. Second, we design a novel cooperative multi-agent system that implicitly captures the imbalanced distribution between abnormality and normality. Experiments on two CXR report datasets show that our method achieves state-of-the-art performance in terms of various evaluation metrics. Our results expose that the proposed approach is able to generate high-quality medical reports through integrating the structure information.