Bridging the Gap: Cross-modal Knowledge Driven Network for Radiology Report Generation

Radiology report generation aims to generate medical reports based on given medical images, which can alleviate the workload of radiologists and has attracted significant research interest in recent years. However, existing studies have struggled to bridge the gap between the two different modalities (i.e. image and text) and generate clinically accurate reports. This is primarily due to the challenges in modelling the crossmodal mappings and the inefficiency of transferring knowledge across modalities. To address these challenges, in this paper, we propose to leverage a pre-constructed knowledge graph as a shared matrix that bridges the gap between visual and textual information, facilitating cross-modal knowledge transfer. This shared knowledge matrix effectively captures cross-modal mappings and aligns information between images and texts, thereby bridging the gap between modalities. Specifically, we propose a new module for knowledge distillation and preservation that integrates relevant knowledge representations into both visual and textual inputs, facilitating intuitive cross-modal knowledge interaction and enhancing the clinical accuracy of the generated reports. Experimental results on two benchmark datasets show the effectiveness of our method, outperforming state-of-the-arts in report generation.