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Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification

Summary

Introducing StenCE, a pretraining framework that uses cross-modal contrastive learning to extract stenosis-related signals from ECGs by aligning them with angiogram features. It enables non-invasive early screening, achieving state-of-the-art performance in severe stenosis classification without relying on invasive angiography.

SourcearXiv Machine LearningAuthor: Nikola Cenikj, \"Ozg\"un Turgut, Alexander M\"uller, Alexander Steger, Jan Kehrer, Marcus Brugger, Daniel Rueckert, Philip M\"uller
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
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[2606.02605] Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification

[Submitted on 23 May 2026]

Title:Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification

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Abstract:Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the standard for stenosis diagnosis, they are invasive, time- and resource-intensive, and therefore only performed on patients with a high probability of disease based on symptoms and prior clinical tests. However, a subset of patients, especially those without symptoms, may remain undiagnosed. Detecting indications of stenosis from ECGs, which are fast, cheap, non-invasive, and thus routinely acquired even in asymptomatic patients, would support early diagnosis. However, as no reliable stenosis-specific signal has been identified in ECGs, they can not currently be used for stenosis risk stratification. To address this, we introduce StenCE, a pretraining framework, allowing stratification of patients based on features derived directly from ECGs. Evaluations across varying stenosis severity thresholds and additional ECG disease classification tasks demonstrate consistent performance improvements across different ECG encoders, outperforming previous work. The obtained models successfully detect signals for stenosis diagnosis in ECGs and are the first to achieve high performance in severe stenosis classification. The source code is available at this https URL.

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)

Cite as: arXiv:2606.02605 [cs.LG]

(or arXiv:2606.02605v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.02605

arXiv-issued DOI via DataCite

Submission history

From: Nikola Cenikj [view email] [v1] Sat, 23 May 2026 14:50:22 UTC (1,490 KB)

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Key points and analysis

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Key points

  • Severe coronary artery stenosis increases heart attack risk but currently requires invasive angiography for diagnosis.
  • ECG is non-invasive but lacks stenosis-specific signals for risk stratification.
  • StenCE uses contrastive learning to learn representations from paired ECG and angiogram data.
  • The framework consistently improves ECG encoders across severity thresholds and outperforms prior methods.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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