2023年4月17日 · 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.

Region-Guided Radiology Report Generation (RGRG): the object detector extracts visual features for 29 unique anatomical regions in the chest. Two subsequent binary classifiers select salient …

We introduce a simple yet effective Region-Guided Radiology Report Generation (RGRG) method that detects anatomical regions and generates individual descriptions for each. Empirical …

The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate t.

2024年9月11日 · 本项目名为“交互式可解释区域引导放射学报告生成”（Interactive and Explainable Region-guided Radiology Report Generation），旨在通过集成智能识别和描述技 …

2024年9月11日 · 本教程旨在帮助开发者了解并快速上手 Region-Guided Radiology Report Generation 开源项目。 我们将详细解析其核心组件，包括项目目录结构、启动文件以及配置文 …

Automated generation of radiology reports from chest X-rays has the potential to substantially reduce the workload of radiologists. Recent advances in report generation using deep learning …

2024年6月25日 · 通过引入先进的区域检测机制，并结合深度学习模型，该系统不仅能够更加精准地描述影像中的关键部位，还能实现交互式的临床应用，显著提升了报告的生成质量与透明度 …

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.

2023年6月1日 · Tanida et al. [27] proposes a straightforward region-guided report generation model that detects anatomical regions and subsequently describes individual salient regions to …