TALON：一种用于放射学报告生成的时间感知纵向框架
TALON: A Temporally Aware Longitudinal Framework for Radiology Report Generation
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中文总结 AI 辅助
提出时间感知纵向框架TALON，通过双通道融合与门控机制自适应整合可变长度病史，在MIMIC-CXR上超越现有方法，提升放射学报告生成的纵向比较能力。
中文摘要 AI 辅助
当前的放射学报告生成（RRG）模型通常基于单次检查或仅最近一次既往检查生成描述性报告，这限制了其进行准确且有意义的纵向比较以及检测细微间隔变化的能力。尽管近期方法已开始纳入多次既往检查，但它们通常聚合固定长度的病史，而未在融合前显式建模每次既往检查的基于角色的相关性。为解决此问题，我们提出了TALON，一种时间感知的纵向RRG框架，可自适应地整合可变长度的患者病史。其底层双通道时间融合模块（DCTFM）通过互补的相似性和变化通道将当前检查与每次既往检查进行比较，分别捕获持续性发现和间隔变化。专门设计的通道特定注意力估计每次既往检查的相关性，而学习到的既往特定门控自适应地整合信息丰富的纵向证据并抑制冗余。在MIMIC-CXR上的实验表明，TALON在各种临床效能和基于图的指标上优于当前最先进的方法。当更多既往检查可用时，TALON在这些指标上的性能进一步提升，这强调了TALON的DCTFM在建模比现有方法更长、更复杂的患者病史的纵向RRG方面的优势。
英文摘要
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 without explicitly modeling the role-dependent relevance of each prior examination before fusion. To address this, we propose TALON, a Temporally Aware LONgitudinal RRG framework that adaptively integrates variable-length patient histories. The underlying Dual-Channel Temporal Fusion Module (DCTFM) compares the current examination with each prior examination through complementary similarity and change channels to capture persistent findings and interval changes, respectively. The specially designed channel-specific attention estimates the relevance of each prior examination, while a learned prior-specific gate adaptively integrates informative longitudinal evidence and suppresses redundancy. Experiments on MIMIC-CXR show that TALON outperforms the current state-of-the-art method on various clinical efficacy and graph-based metrics. When more prior examinations become available, TALON's performance on these metrics improves even further, emphasizing the strength of TALON's DCTFM in modeling longitudinal RRG across longer and more complex patient histories than existing approaches.
