A frame-wise masked autoencoder with a temporal contrastive loss achieves 0.88 AUROC for binary EF classification on EchoNet-Dynamic, below the cited 0.93 AUROC of ECHO-VISION-FM.
Video MAE : Masked autoencoders are data-efficient learners for self-supervised video pre-training
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Temporal Representation Learning for Real-Time Ultrasound Analysis
A frame-wise masked autoencoder with a temporal contrastive loss achieves 0.88 AUROC for binary EF classification on EchoNet-Dynamic, below the cited 0.93 AUROC of ECHO-VISION-FM.