LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation
This work addresses the need for efficient breast tumor segmentation in clinical settings, though it is incremental as it builds on existing knowledge distillation and U-Net architectures.
The authors tackled the problem of high computational complexity in deep learning-based breast tumor segmentation from ultrasound images by proposing LightBTSeg, a lightweight model using dual-path joint knowledge distillation, which achieved superior performance on benchmark datasets.
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.