Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports
This paper presents a training-free transition-aware best-of-N sampling scheme for pre-trained chest X-ray report generators. It splits reports into sentences, embeds them as sets, computes directional vectors between prior and current, and scores candidates via cosine distance to ground-truth transition vectors. Evaluated on multiple generators, it outperforms random selection, especially on the Impression section.
[2606.28393] Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports
[Submitted on 23 Jun 2026]
Title:Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports
View a PDF of the paper titled Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports, by Halil Ibrahim Gulluk and Max Van Puyvelde and Wim Van Criekinge and Olivier Gevaert
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Abstract:In longitudinal clinical practice, every chest X-ray is read in the context of the patients prior exam, and much of what the radiologist communicates is the change from one visit to the next. To the best of our knowledge, we present the first training-free best-of-N sampling scheme for pre-trained chest X-ray report generators that is explicitly aware of this longitudinal prior to current transition. We call it transition-aware best-of-N sampling, each report is split into sentences and embedded into an unordered set in Rd; each (prior, current) pair is reduced to a fixed-dim directional vector via a set-to-set distance designed to encode the change between the two sets; and candidates are scored by cosine distance from their candidate transition vector to a cached bank of ground-truth training transition vectors, aggregated as min or kNN. We instantiate the framework with four directional set distances (mean-shift, novelty residual, directed-Hausdorff anchor, and cost-weighted optimal transport) and evaluate on a multi-visit AP-PA cohort, running inference under three prompts on three vision-language generators. Transition-aware best-of-N outperforms random selection across the board, with the largest relative gains on the Impression section.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.28393 [cs.CV]
(or arXiv:2606.28393v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.28393
arXiv-issued DOI via DataCite
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From: Halil Ibrahim Gulluk [view email] [v1] Tue, 23 Jun 2026 23:11:59 UTC (1,128 KB)
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