![[2602.21935] A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.21935] A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography
Summary
This article presents a framework for coronary artery calcium (CAC) scoring that generalizes across gated and non-gated CT scans, enhancing cardiovascular risk assessment.
Why It Matters
The ability to perform CAC scoring using non-gated CT scans could significantly improve cardiovascular screening accessibility, as it eliminates the need for specialized imaging equipment. This advancement could lead to earlier detection and treatment of cardiovascular diseases, ultimately saving lives and reducing healthcare costs.
Key Takeaways
- Introduces CARD-ViT, a self-supervised Vision Transformer for CAC scoring.
- Achieves comparable accuracy on non-gated scans without requiring additional training data.
- Demonstrates the potential for scalable cardiovascular screening using routine chest imaging.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21935 (cs) [Submitted on 25 Feb 2026] Title:A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography Authors:Mahmut S. Gokmen, Moneera N. Haque, Steve W. Leung, Caroline N. Leach, Seth Parker, Stephen B. Hobbs, Vincent L. Sorrell, W. Brent Seales, V. K. Cody Bumgardner View a PDF of the paper titled A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography, by Mahmut S. Gokmen and 8 other authors View PDF HTML (experimental) Abstract:Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans. At its core is CARD-ViT, a self-supervised Vision Transformer trained exclusively on gated CT data using DINO. Without any non-gated training data, our framework achieves 0.707 accuracy and a Cohen's kappa of 0.528 on the Stanford non-gated dataset, matching models trained directly on non-gated scans. On gated test sets, the framework achieves 0.910 accuracy with Cohen's kappa scores of 0.871 and 0.874 across independent datasets, demonstrating robust risk stratification. These results demonstrate the feasibility of cros...