arxiv: 2605.11304 · v1 · submitted 2026-05-11 · 💻 cs.CV

Recognition: no theorem link

CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography

Eva Prakash , Yunhe Gao , Chong Wang , Justin Xu , Neal Prakash , Arne Michalson , Seena Dehkharghani , Eun Kyoung Hong

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Julie Bauml Roger Boodoo Jean-Benoit Delbrouck Sophie Ostmeier Curtis Langlotz

Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:58 UTC · model grok-4.3

classification 💻 cs.CV

keywords chest radiographytemporal reasoningvision-language modelsprogression classificationspatial groundinglongitudinal changepaired studies

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The pith

Current vision-language models for chest X-rays exhibit consistent limitations in spatial grounding and fine-grained temporal reasoning over paired prior and current studies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CheXTemporal, a dataset of paired prior-current chest radiographs with finding-level temporal annotations using a five-class progression taxonomy of new, worse, stable, improved, and resolved. It also provides a 280K-pair silver dataset with automatic labels for large-scale testing. Evaluations of state-of-the-art models in zero-shot settings show they perform better on salient categories such as worse than on subtle states such as stable and resolved, while struggling with spatial alignment and distribution shifts. This matters because clinical chest radiograph interpretation routinely requires tracking longitudinal disease changes across multiple studies. If the observed limitations hold, models will continue to miss clinically relevant temporal evolution in practice.

Core claim

Current state-of-the-art vision-language CXR models exhibit consistent limitations in spatial grounding, fine-grained temporal reasoning, and robustness under distribution shift. Models perform substantially better on salient progression categories such as worse than on temporally subtle states such as stable and resolved.

What carries the argument

The five-class progression taxonomy applied to explicitly aligned prior-current chest X-ray pairs, with localized spatial supervision of pathology and multi-source coverage for cross-domain testing.

Load-bearing premise

The five-class progression taxonomy and the automatically derived silver labels accurately reflect clinically meaningful temporal changes without substantial annotation noise or misalignment between studies.

What would settle it

A radiologist review of a random sample of silver-labeled pairs that finds high disagreement rates on the stable and resolved classes.

Figures

Figures reproduced from arXiv: 2605.11304 by Arne Michalson, Chong Wang, Curtis Langlotz, Eun Kyoung Hong, Eva Prakash, Jean-Benoit Delbrouck, Julie Bauml, Justin Xu, Neal Prakash, Roger Boodoo, Seena Dehkharghani, Sophie Ostmeier, Yunhe Gao.

**Figure 1.** Figure 1: Manual analysis of 55 random cases per dataset in the silver dataset. Progression and anatomy accuracies are computed at the finding level; static and dynamic accuracies are computed at the sentence level. used in the gold dataset: new, worse, stable, improved, or resolved. Second, it labels report sentences as static if they describe the current study alone or dynamic if they express temporal comparison o… view at source ↗

**Figure 2.** Figure 2: Representative grounding heatmaps across models on the gold dataset. Examples include [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

read the original abstract

Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-report pairs and lack explicit supervision for modeling longitudinal change. We introduce CheXTemporal, a dataset for temporally grounded reasoning in chest radiography consisting of paired prior-current chest X-rays (CXR) with finding-level temporal and spatial annotations. The dataset includes a five-class progression taxonomy (new, worse, stable, improved, resolved), localized spatial supervision of pathology, explicit spatial-temporal alignment across paired studies, and multi-source coverage for cross-domain evaluation. We additionally construct a 280K-pair silver dataset with automatically derived temporal and anatomical supervision for large-scale evaluation under weaker supervision. Using these resources, we evaluate multiple state-of-the-art vision-language CXR models on grounding and progression-classification tasks in a zero-shot setting. Across both gold and silver evaluations, current models exhibit consistent limitations in spatial grounding, fine-grained temporal reasoning, and robustness under distribution shift. In particular, models perform substantially better on salient progression categories such as worse than on temporally subtle states such as stable and resolved, suggesting limited modeling of longitudinal disease evolution in chest radiography.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CheXTemporal supplies a new paired CXR dataset with temporal progression labels that exposes clear gaps in current models on subtle longitudinal changes.

read the letter

The core contribution is a dataset of prior-current chest X-ray pairs annotated with a five-class progression taxonomy, spatial localization of findings, and explicit alignment between studies. They also release a 280K-pair silver set built from automatic temporal and anatomical rules. Zero-shot tests on several vision-language CXR models show the expected pattern: stronger performance on salient classes like worse, weaker on stable and resolved. This lines up with the clinical reality that longitudinal monitoring often hinges on detecting small shifts rather than dramatic ones. The work fills a real hole because almost all prior CXR vision-language resources are single-study only. The multi-source coverage and dual gold-silver tracks are practical additions that let people test both high-quality and scale settings. The main soft spot is the silver label construction. Automatic derivation from the taxonomy can inject noise or bias, especially on low-salience categories where registration error or threshold choices would matter most. If that noise is present, the reported gap between classes could be partly artifactual. The abstract does not spell out inter-rater numbers or validation steps for the gold set, so the strength of the gold results is still hard to judge from what is shown. This paper is aimed at groups building or benchmarking temporal models for radiology. Anyone working on longitudinal disease tracking or medical vision-language evaluation will get direct use from the resource and the baseline numbers. It deserves a serious referee because the dataset itself is new, the task is clinically grounded, and the evaluation points to a concrete limitation worth addressing. I would send it to review with instructions to examine the silver-label heuristics and any agreement statistics in detail.

Referee Report

2 major / 2 minor

Summary. The paper introduces CheXTemporal, a dataset for temporally-grounded reasoning in chest radiography consisting of paired prior-current chest X-rays with finding-level temporal and spatial annotations using a five-class progression taxonomy (new, worse, stable, improved, resolved), explicit spatial-temporal alignment, and multi-source coverage. It also constructs a 280K-pair silver dataset with automatically derived labels and benchmarks state-of-the-art vision-language models on grounding and progression-classification tasks in zero-shot settings, reporting consistent limitations in spatial grounding, fine-grained temporal reasoning, and robustness under distribution shift, with substantially better performance on salient categories such as worse than on subtle states such as stable and resolved.

Significance. If the gold and silver annotations prove reliable, this dataset would fill a clinically relevant gap in longitudinal CXR analysis and supply a useful benchmark for improving vision-language models on temporal change detection. The dual gold/silver design and cross-domain testing add practical value for assessing robustness, while the reported class-specific performance gaps highlight actionable model weaknesses in modeling disease evolution.

major comments (2)

[§3.2] §3.2 (Silver Label Construction): The automatic derivation of silver labels from the five-class taxonomy with temporal and anatomical supervision risks systematic noise or bias that could artifactually produce the reported gaps on subtle classes. The paper must detail the exact heuristics for progression inference, prior-current registration, and change detection thresholds, plus quantitative agreement metrics between silver and gold labels, to confirm that the fine-grained temporal reasoning deficits reflect genuine model limitations rather than label quality issues.
[§3.1] §3.1 (Gold Annotations): The manuscript provides no details on annotation validation, inter-rater agreement statistics, or statistical tests for the gold annotations and spatial labels. This information is load-bearing for the central claim that current models exhibit consistent limitations, as the five-class taxonomy underpins all evaluations.

minor comments (2)

[Abstract] Abstract: The claim of 'multi-source coverage for cross-domain evaluation' should name the specific sources or domains to improve clarity.
[Evaluation] Evaluation section: Specify the exact prompts and any prompt engineering used for the zero-shot VLM evaluations to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which identify important areas for improving the clarity and rigor of our dataset descriptions. We address each major comment point-by-point below and will incorporate the requested details in the revised manuscript.

read point-by-point responses

Referee: [§3.2] §3.2 (Silver Label Construction): The automatic derivation of silver labels from the five-class taxonomy with temporal and anatomical supervision risks systematic noise or bias that could artifactually produce the reported gaps on subtle classes. The paper must detail the exact heuristics for progression inference, prior-current registration, and change detection thresholds, plus quantitative agreement metrics between silver and gold labels, to confirm that the fine-grained temporal reasoning deficits reflect genuine model limitations rather than label quality issues.

Authors: We agree that the current manuscript provides insufficient detail on the silver label construction, which is necessary to substantiate that the reported performance gaps (particularly on subtle classes) arise from model limitations rather than label artifacts. In the revised version, we will expand §3.2 with a precise description of the heuristics: the rules for inferring each of the five progression classes from report text and image-derived features, the registration algorithm and alignment procedure for prior-current pairs, and the specific change detection thresholds (e.g., size or intensity deltas for pathology). We will also report quantitative agreement metrics between silver and gold labels on an overlapping validation subset, including overall accuracy, per-class precision/recall, and Cohen's kappa. These additions will allow readers to evaluate potential systematic biases. revision: yes
Referee: [§3.1] §3.1 (Gold Annotations): The manuscript provides no details on annotation validation, inter-rater agreement statistics, or statistical tests for the gold annotations and spatial labels. This information is load-bearing for the central claim that current models exhibit consistent limitations, as the five-class taxonomy underpins all evaluations.

Authors: We acknowledge the omission of annotation validation details in the manuscript. The gold annotations were produced by multiple board-certified radiologists using a standardized protocol with explicit guidelines for the five-class taxonomy and spatial grounding. In the revision, we will expand §3.1 to describe the full annotation workflow, including the number of annotators per case, the adjudication process for disagreements, inter-rater agreement statistics (Fleiss' kappa for progression classes and Dice/IoU for spatial labels), and any statistical tests (e.g., for label consistency across sources). This will directly support the reliability of the evaluations and the central claims about model limitations. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset construction and zero-shot benchmarking are self-contained

full rationale

The paper introduces CheXTemporal with gold annotations and an explicitly constructed 280K-pair silver dataset using automatic derivation of temporal labels from a five-class taxonomy. It then reports zero-shot evaluations of external pre-trained vision-language models on grounding and progression tasks. No equations, fitted parameters, self-citations forming load-bearing chains, or derivations appear in the manuscript. The silver-label construction is presented transparently as heuristic and weaker supervision rather than as a derived prediction that reduces to its own inputs. All reported performance gaps are empirical observations on held-out models and data splits, with no mathematical reduction to the paper's own construction steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The contribution rests on the creation of a new dataset and taxonomy rather than on mathematical axioms or fitted parameters.

invented entities (1)

five-class progression taxonomy (new, worse, stable, improved, resolved) no independent evidence
purpose: Categorize temporal changes in radiographic findings across paired studies
This taxonomy is newly defined by the paper to structure the annotations.

pith-pipeline@v0.9.0 · 5550 in / 1222 out tokens · 54318 ms · 2026-05-13T05:58:14.352159+00:00 · methodology

discussion (0)

Reference graph

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