DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting

Abstract

Automatic radiology report generation has attracted considerable attention with the rise of computer-aided diagnostic systems. Due to the inherent biases in medical imaging data, generating reports with precise clinical details is challenging yet crucial for accurate diagnosis. To this end, we design a disease description graph that encapsulates comprehensive and pertinent disease information. By aligning visual features with the graph, our model enhances the quality of the generated reports. Furthermore, we introduce a novel informed prompting method which increases the accuracy of short-gram predictions, acting as an implicit bag-of-words planning for surface realization. Notably, this informed prompt succeeds with a three-layer decoder, reducing the reliance on conventional prompting methods that require extensive model parameters. Extensive experiments on two widely-used datasets, IU-Xray and MIMIC-CXR, demonstrate that our method outperforms previous state-of-the-art models.