SaTransformer: Semantic-aware transformer for breast cancer classification and segmentation

Breast cancer classification and segmentation play an important role in identifying and detecting benign and malignant breast lesions. However, segmentation and classification still face many challenges: 1) The characteristics of cancer itself, such as fuzzy edges, complex backgrounds, and significant changes in size, shape, and intensity distribution make accurate segment and classification challenges. 2) Existing methods ignore the potential relationship between classification and segmentation tasks, due to the classification and segmentation being treated as two separate tasks. To overcome these challenges, in this paper, a novel Semantic-aware transformer (SaTransformer) for breast cancer classification and segmentation is proposed. Specifically, the SaTransformer enables doing the two takes simultaneously through one unified framework. Unlike existing well-known methods, the segmentation and classification information are semantically interactive, reinforcing each other during feature representation learning and improving the ability of feature representation learning while consuming less memory and computational complexity. The SaTransformer is validated on two publicly available breast cancer datasets - BUSI and UDIAT. Experimental results and quantitative evaluations (accuracy: 97.97%, precision: 98.20%, DSC: 86.34%) demonstrate that the SaTransformer outperforms other state-of-the-art methods.

Automated summary

Breast cancer classification and segmentation are crucial in identifying and detecting benign and malignant breast lesions. However, these tasks face challenges due to the characteristics of cancer itself and the lack of consideration of their potential relationship. To address these challenges, this paper proposes a novel Semantic-aware transformer (SaTransformer) that performs classification and segmentation simultaneously through a unified framework.