Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representations that can be transferred to downstream tasks. However, MAE has not been thoroughly explored in ultrasound imaging. In this work, we investigate the potential of MAE for ultrasound image recognition. Motivated by the unique property of ultrasound imaging in high noise-to-signal ratio, we propose a novel deblurring MAE approach that incorporates deblurring into the proxy task during pretraining. The addition of deblurring facilitates the pretraining to better recover the subtle details presented in the ultrasound images, thus improving the performance of the downstream classification task. Our experimental results demonstrate the effectiveness of our deblurring MAE, achieving state-of-the-art performance in ultrasound image classification. Overall, our work highlights the potential of MAE for ultrasound image recognition and presents a novel approach that incorporates deblurring to further improve its effectiveness.

翻译：掩码自编码器（MAE）在众多视觉任务中备受关注并取得了卓越性能。它在预训练阶段重建随机掩码的图像块（即代理任务），从而学习可迁移至下游任务的有意义语义表征。然而，MAE在超声成像中的潜力尚未得到充分探索。本研究针对超声图像识别任务，深入探究了MAE的适用性。受超声成像高噪声信号比这一独特特性的启发，我们提出了一种创新的去模糊MAE方法，该方法在预训练阶段将去模糊机制融入代理任务。引入去模糊可促进预训练过程更有效地恢复超声图像中的细微细节，从而提升下游分类任务的性能。实验结果表明，我们的去模糊MAE方法在超声图像分类任务中取得了最佳性能。总体而言，本工作不仅揭示了MAE在超声图像识别中的潜力，还提出了一种融合去模糊机制以增强其效用的创新方案。