BreastLightSAM: a lightweight pipeline for fast and accurate breast cancer diagnosis

BreastLightSAM is a lightweight pipeline developed for rapid and precise breast cancer diagnosis utilizing 2D ultrasound images. The model incorporates an optimized Segmentation Anything Model (SAM) with a RepViT-based encoder for segmentation, and a compact classification module. This integration yields high accuracy, achieving a mean Dice Similarity Coefficient (DSC) of 0.812 and a mean pixel accuracy of 0.943, with minimal latency of just 28.6 milliseconds, rendering it suitable for mobile and resource-constrained environments. Through the incorporation of human-like prompt design, BreastLightSAM enhances robustness and generalization, demonstrating significant potential for real-time clinical applications.