arXiv 2302.138692023-08-03eess.IVcs.CV

EDMAE：一种用于小儿超声心动图标准切面识别的高效解耦掩码自编码器

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

Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海儿童医学中心）；Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP)（上海智能儿科工程技术研究中心）；East China Normal University（华东师范大学）；University of Shanghai for Science and Technology（上海理工大学）

Yiman Liu, Xiaoxiang Han, Tongtong Liang, Bin Dong, Jiajun Yuan, Menghan Hu, Qiaohong Liu, Jiangang Chen, Qingli Li, Yuqi Zhang

备注：: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Control
发表信息：: Biomedical Signal Processing and Control 86 (2023) 105280
DOI：: 10.1016/j.bspc.2023.105280

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AI总结：

本文提出了一种新型自监督方法EDMAE，通过基于教师-学生编码器的解耦掩码自编码器结构并采用纯卷积操作，有效提升了小儿超声心动图标准切面识别的准确性与训练效率，并在多个下游任务中表现出竞争力。

摘要：

本文介绍了高效解耦掩码自编码器（EDMAE），这是一种用于识别小儿超声心动图标准切面的新型自监督方法。EDMAE引入了基于编码器-解码器结构的新代理任务。EDMAE编码器由教师编码器和学生编码器组成。教师编码器提取掩码图像块的潜在表示，而学生编码器提取可见图像块的潜在表示。计算两个编码器输出的特征图之间的损失，以确保它们提取的潜在表示的一致性。EDMAE在MAE编码器中使用纯卷积操作代替ViT结构。这提高了训练效率和收敛速度。EDMAE在使用自监督学习的大规模小儿超声心动图私有数据集上进行预训练，然后针对标准切面识别进行微调。所提出的方法在小儿超声心动图的27个标准切面中实现了高分类准确率。为了进一步验证所提出方法的有效性，作者在公开数据集CAMUS上执行了心脏超声分割的另一项下游任务。实验结果表明，所提出的方法优于一些流行的监督方法和近期的自监督方法，并且在不同的下游任务上更具竞争力。

英文摘要：

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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