Ultrasound image classification of breast tumors based on deblurring masked autoencoder

With the development of computer technology, there is growing interest in developing deep learning systems to assist doctors in diagnosis. In specific clinical applications, the existing deep learning methods are faced with many problems. One of the most important problems is the lack of large and reliable labeled datasets, since the annotation of medical images requires professional knowledge. Furthermore, medical pictures include a lot of noise and are more erratic and blurry, making detection more challenging. This research attempts to create a deep learning-based system for classifying benign and malignant breast cancers, which can help physicians diagnose patients more accurately, increase productivity, and lower the risk of misdiagnosis. Breast ultrasonography is a common diagnostic tool for breast malignancies. Our work suggests that ultrasound pictures of breast cancers may be classified using the deblurring masked autoencoder. During pretraining, this technique adds deblurring to the proxy job of MAE, which is more suitable for tumor classification task based on ultrasound images. Our experimental findings show the model we propose works well, which achieves an AUROC score of 93.89 on large dataset and an AUROC score of 88.45 on small dataset, resulting in state-of-the-art performance in the ultrasound picture categorization of breast cancers.