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

• URL: http://arxiv.org/abs/2302.13869v3
• Date: Thu, 3 Aug 2023 07:46:03 GMT
• Title: EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
• Authors: Yiman Liu, Xiaoxiang Han, Tongtong Liang, Bin Dong, Jiajun Yuan, Menghan Hu, Qiaohong Liu, Jiangang Chen, Qingli Li, Yuqi Zhang
• Abstract summary: The Efficient Decoupled Masked Autoencoder (EDMAE) is a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography.
• Score: 16.215207742732893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.

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