Weakly Supervised Breast Ultrasound Image Segmentation Based on Image Selection

Automatic segmentation in Breast Ultrasound (BUS) imaging is vital to BUS computer-aided diagnostic systems. Fully supervised learning approaches can attain high accuracy, yet they depend on pixel-level annotations that are challenging to obtain. As an alternative, weakly supervised learning methods offer a way to lessen the dependency on extensive annotation requirements. Existing weakly supervised learning methods are typically trained on the entire dataset, but not all samples are effective in training a robust image segmentation model. To overcome this challenge, we have developed a new weakly supervised learning approach for BUS image segmentation. Our framework includes three key contributions: 1) A novel image selection method using Class Activation Maps is proposed to identify high-quality candidates for generating pseudo-segmentation labels; 2) The 'Segment Anything' is utilized for pseudo-label generation; 3) A segmentation model is trained using a Mean Teacher method, incorporating both pseudo-labeled and non-labeled images. The proposed framework is evaluated on a public BUS image dataset and achieves an Intersection over Union score that is 82.9% of what is attained by fully supervised methods.