Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition
This work addresses ultrasound image recognition, a domain-specific problem with high noise-to-signal ratio, presenting an incremental improvement by adapting MAE with deblurring.
The authors tackled ultrasound image recognition by proposing a deblurring masked autoencoder (MAE) that incorporates deblurring into pretraining to recover subtle details, achieving state-of-the-art performance in classification.
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.