e12571 Background: Breast cancer remains a leading cause of cancer-related morbidity worldwide, with imaging central to early diagnosis and treatment planning. Ultrasonography is widely used due to its safety and effectiveness in dense breast tissue; however, interpretation is operator dependent and varies across clinical settings. Although deep learning has advanced ultrasound-based cancer detection, many high-performing architectures impose substantial training and inference costs, limiting sc