Dilated transformer: residual axial attention for breast ultrasound image segmentation.

ABSTRACT:

Background

The segmentation of breast ultrasound (US) images has been a challenging task, mainly due to limited data and the inherent image characteristics involved, such as low contrast and speckle noise. Although convolutional neural network-based (CNN-based) methods have made significant progress over the past decade, they lack the ability to model long-range interactions. Recently, the transformer method has been successfully applied to the tasks of computer vision. It has a strong ability to capture distant interactions. However, most transformer-based methods with excellent performance rely on pre-training on large datasets, making it infeasible to directly apply them to medical images analysis, especially that of breast US images with limited high-quality labels. Therefore,

SUBMITTER: Shen X 

PROVIDER: S-EPMC9403584 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature