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The extraction of structured clinical information from free-text radiologyreports in the form of radiology graphs has been demonstrated to be a valuableapproach for evaluating the clinical correctness of report-generation methods.However, the direct generation of radiology graphs from chest X-ray (CXR)images has not been attempted. To address this gap, we propose a novel approachcalled Prior-RadGraphFormer that utilizes a transformer model with priorknowledge in the form of a probabilistic knowledge graph (PKG) to generateradiology graphs directly from CXR images. The PKG models the statisticalrelationship between radiology entities, including anatomical structures andmedical observations. This additional contextual information enhances theaccuracy of entity and relation extraction. The generated radiology graphs canbe applied to various downstream tasks, such as free-text or structured reportsgeneration and multi-label classification of pathologies. Our approachrepresents a promising method for generating radiology graphs directly from CXRimages, and has significant potential for improving medical image analysis andclinical decision-making.

Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays