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:自动生成放射学报告有助于辅助放射科医生在繁琐的报告撰写中节约时间。现有方法从图像级特征生成完整的报告,未明确关注图像中的解剖区域。本文提出了一种简单而有效的区域引导报告生成模型,对解剖区域进行检测,然后描述单个显著区域以形成最终报告。虽然之前的方法在生成报告时没有人机交互的可能性,并且解释能力有限,但我们的方法通过额外的交互性功能开启了新的临床应用,并引入了高度的透明度和可解释性。全面的实验展示了我们的方法在报告生成方面的有效性,优于之前的最新模型,并突出了其交互性能。代码和检查点可在 https://github.com/ttanida/rgrg 获取。