EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
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We propose an efficient decoupled mask autoencoder (EDMAE) for standard view recognition in Pediatric Echocardiography, which is an unsupervised (or self-supervised) method. By building a novel proxy task, EDMAE is pretrained on a large-scale unlabeled pediatric cardiac ultrasound dataset to achieve excellent performance in downstream tasks of standard plane recognition. EDMAE improves training efficiency by using pure convolutional operations, and forces the encoder to extract more and higher quality semantic information by decoupling the encoder and decoder. Extensive experiments have demonstrated the effectiveness of the proposed method.
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