{"ID":3050107,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T10:55:11.836960423Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04722","arxiv_id":"2606.04722","title":"StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT","abstract":"Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often uncertain in clinical practice, necessitating imaging-based assessment of tissue age as a surrogate marker. Early ischemic changes on routinely acquired non-contrast CT (NCCT) are often subtle, and real-world clinical datasets exhibit pronounced onset-time class imbalance and center-scanner-related heterogeneity. In this work, we propose StrokeTimer, a fully automated framework for onset-time estimation in acute ischemic stroke. StrokeTimer integrates self-supervised disentanglement learning with energy-guided contrastive learning to capture subtle ischemic patterns while addressing long-tailed data distributions under acquisition variability. Onset time is categorized into three clinically relevant windows: \u003c4.5 h, 4.5-6 h, and \u003e6 h. Experimental results on a large multi-center NCCT dataset from two national cohorts, MR CLEAN Registry and MR CLEAN LATE, show that StrokeTimer achieves a macro AUC of 0.69 and a macro F1-score of 0.57, improving the strongest baseline by nearly 50% (p \u003c 0.005). In this realistic, challenging setting, representative baseline approaches exhibit near-chance macro performance. Model explanations further highlight subtle gray-white matter blurring and hypodense regions consistent with established radiological biomarkers. These findings demonstrate the potential of StrokeTimer to support treatment decision-making in acute ischemic stroke. Code is available at https://github.com/BrainVas/StrokeTimer.","short_abstract":"Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often uncertain in clinical practice, necessitating imaging-based assessment of tissue age as a...","url_abs":"https://arxiv.org/abs/2606.04722","url_pdf":"https://arxiv.org/pdf/2606.04722v1","authors":"[\"Weiru Wang\",\"Susanne G. H. Olthuis\",\"Elizaveta Lavrova\",\"Robert J. van Oostenbrugge\",\"Charles B. L. M. Majoie\",\"Wim H. van Zwam\",\"Ruisheng Su\"]","published":"2026-06-03T11:01:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":612781,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-04T02:13:16.786527022Z","DeletedAt":null,"paper_id":3050107,"paper_url":"https://arxiv.org/abs/2606.04722","paper_title":"StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT","repo_url":"https://github.com/BrainVas/StrokeTimer","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}