A self-supervised contrastive masked autoencoder pre-training scheme for UNet improves coronary artery segmentation from X-ray angiography in low-data settings.
Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images,
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CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography
A self-supervised contrastive masked autoencoder pre-training scheme for UNet improves coronary artery segmentation from X-ray angiography in low-data settings.