The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://github.com/ttanida/rgrg .

本研究提出了一种基于区域引导的报告生成模型，该模型能够检测解剖区域并生成具有人类可读性和可解释性的报告，通过额外的交互能力和更高的透明度和可解释性，扩展了新的临床应用案例。实验结果表明，该方法在报告生成方面具有非常出色的性能，并在交互性能方面表现突出。

交互式及可解释的区域引导放射学报告生成