EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

An efficient decoupled masked autoencoder (EDMAE), which is a novel self-supervised method is proposed for standard view recognition in pediatric echocardiography in this paper. The proposed EDMAE based on the encoder-decoder structure forms a new proxy task. The decoder of EDMAE consists of a teacher encoder and a student encoder, in which the teacher encoder extracts the latent representation of the masked image blocks, while the student encoder extracts the latent representation of the visible image blocks. A loss is calculated between the feature maps output from two encoders to ensure consistency in the latent representations they extracted. EDMAE replaces the VIT structure in the encoder of traditional MAE with pure convolution operation to improve training efficiency. EDMAE is pre-trained in a self-supervised manner on a large-scale private dataset of pediatric echocardiography, and then fine-tuned on the downstream task of standard view recognition. The high classification accuracy is achieved in 27 standard views of pediatric echocardiography. To further validate the effectiveness of the proposed method, another downstream task of cardiac ultrasound segmentation is performed on a public dataset CAMUS. The experiments show that the proposed method not only can surpass some recent supervised methods but also has more competitiveness on different downstream tasks.