CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography
Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols. In this work we demonstrate a new benchmark for the assessment of deep learning models which densely classify pixels of coronary angiograms to one of SYNTAX classes (or background). The evaluation covers 24 distinct architectures starting with classic convnets to recent state-space-based vision algorithms. We release CARDIAG - a multi-center, multi-label dataset which we carefully split to reliably compute metrics, accounting for diameter error, overlap, centerline quality and calibration. The data contains SYNTAX labels, bina...
| Authors | Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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