  The accurate segmentation of breast tumors is an important prerequisite for
lesion detection, which has significant clinical value for breast tumor
research. The mainstream deep learning-based methods have achieved a
breakthrough. However, these high-performance segmentation methods are
formidable to implement in clinical scenarios since they always embrace high
computation complexity, massive parameters, slow inference speed, and huge
memory consumption. To tackle this problem, we propose LightBTSeg, a dual-path
joint knowledge distillation framework, for lightweight breast tumor
segmentation. Concretely, we design a double-teacher model to represent the
fine-grained feature of breast ultrasound according to different semantic
feature realignments of benign and malignant breast tumors. Specifically, we
leverage the bottleneck architecture to reconstruct the original Attention
U-Net. It is regarded as a lightweight student model named Simplified U-Net.
Then, the prior knowledge of benign and malignant categories is utilized to
design the teacher network combined dual-path joint knowledge distillation,
which distills the knowledge from cumbersome benign and malignant teachers to a
lightweight student model. Extensive experiments conducted on breast ultrasound
images (Dataset BUSI) and Breast Ultrasound Dataset B (Dataset B) datasets
demonstrate that LightBTSeg outperforms various counterparts.
