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 .

翻译：放射学报告的自动生成有望协助放射科医生完成耗时报告撰写任务。现有方法从图像级特征生成完整报告，未能明确关注图像中的解剖区域。我们提出一种简单而有效的区域引导报告生成模型，该模型检测解剖区域，随后描述各个显著区域以形成最终报告。尽管以往方法在生成报告时无法实现人工干预且可解释性有限，我们的方法通过额外的交互能力开辟了新颖的临床应用场景，并引入高度透明性与可解释性。综合实验表明，该方法在报告生成方面具有显著效果，超越先前最先进模型，并凸显其交互能力。代码与检查点文件已发布于 https://github.com/ttanida/rgrg。