[Submitted on 22 Jul 2026]
Title:TALON: A Temporally Aware Longitudinal Framework for Radiology Report Generation
View a PDF of the paper titled TALON: A Temporally Aware Longitudinal Framework for Radiology Report Generation, by Nien-Tsyr Sun and 3 other authors
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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 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.
Subjects:
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2609.20826 [cs.CL]
(or arXiv:2609.20826v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.20826
arXiv-issued DOI via DataCite
Submission history
From: Vincent S. Tseng [view email] [v1] Wed, 22 Jul 2026 12:55:33 UTC (709 KB)
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