StenCE uses cross-modal contrastive learning on paired ECG-angiography data to learn ECG features that classify severe coronary stenosis, reporting the first high performance on this task.
BMC Medical Informatics and Decision Making24(11 2024)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
StenCE uses cross-modal contrastive learning on paired ECG-angiography data to learn ECG features that classify severe coronary stenosis, reporting the first high performance on this task.