TALON：面向放射学报告生成的时间感知纵向框架
原标题：TALON: A Temporally Aware Longitudinal Framework for Radiology Report Generation
AI 摘要
研究团队提出 TALON，一个面向放射学报告生成（RRG）的时间感知纵向框架，通过双通道时间融合模块（DCTFM）分别建模当前检查与各历史检查之间的相似性和变化，并自适应估计每个历史检查的相关性。在 MIMIC-CXR 数据集上，TALON 在临床效能指标和 RadGraph-F1 上超越当前最优方法，且当可用历史检查更多时性能进一步提升。该工作旨在解决现有方法难以捕捉细微间隔变化、无法显式建模历史检查角色相关性的问题。
正文节选
TALON: A Temporally Aware Longitudinal Framework for Radiology Report Generation Abstract Current radiology report generation (RRG) models usually produce descriptive reports based on a single examination or only the most recent prior examination, limiting their ability to perform accurate and meaningful longitudinal comparisons and detect subtle interval changes. Although recent approaches have begun to incorporate multiple prior examinations, they usually aggregate a fixed-length history witho