Self-supervised contrastive learning technique for classifying carotid plaques from ultrasound images under label scarcity. In a pretext task, a triplet network takes three augmented views (strong- and weak-augmentation) of each image and promotes their similarity from both feature- and instance-level perspectives to learn effective plaque representations; the resulting encoder is then fine-tuned on labeled ultrasound images for the downstream classification task. FeaCL achieved 83.4% classification accuracy using only 30% of the training data -- a 16.3% improvement over the same network trained without the self-supervised pretext task.

Self-supervised learning method for carotid plaque segmentation from B-mode ultrasound images, designed to reduce the amount of expert-labeled data needed to train a segmentation network. A level-set-and-least-squares-based deformation procedure synthesizes registration image pairs from unlabeled carotid ultrasound images, and a spatial-transformer-based registration pretext task pretrains a Stacked U-Net (Su-Net) to focus on plaque-contour features before fine-tuning on a small labeled dataset for total plaque area (TPA) segmentation. Evaluated on carotid ultrasound datasets from two different institutions and countries, the method showed robust generalization when trained with only a small number of labeled images.