EchoCLR

Multi-instance contrastive self-supervised learning framework for echocardiography video representation learning: two distinct videos from the same patient exam are treated as positive pairs (rather than augmentations of a single clip), and a frame-reordering pretext task on temporally shuffled frames encourages the 3D-CNN backbone to learn temporally coherent representations. After self-supervised pretraining on unlabeled echocardiograms, the backbone is efficiently fine-tuned for cardiac disease classification (severe aortic stenosis, left ventricular hypertrophy) using very few labeled examples, outperforming SimCLR, standard multi-instance SimCLR, and Kinetics-400-initialized baselines across a range of training-data ratios.