Accurate tumor segmentation and classification in breast ultrasound (BUS) imaging remain challenging due to low contrast, speckle noise, and diverse lesion morphology. This study presents a multi-task deep learning framework that jointly performs lesion segmentation and diagnostic classification using embeddings from the Segment Anything Model (SAM) vision encoder. Unlike prompt-based SAM variants, our approach employs a prompt-free, fully supervised adaptation where high-dimensional SAM features are decoded through either a lightweight convolutional head or a UNet-inspired decoder for pixel-wise segmentation. The classification branch is enhanced via mask-guided attention, allowing the model to focus on lesion-relevant features while suppressing background artifacts. Experiments on the PRECISE 2025 breast ultrasound dataset, split per class into 80 percent training and 20 percent testing, show that the proposed method achieves a Dice Similarity Coefficient (DSC) of 0.887 and an accuracy of 92.3 percent, ranking among the top entries on the PRECISE challenge leaderboard. These results demonstrate that SAM-based representations, when coupled with segmentation-guided learning, significantly improve both lesion delineation and diagnostic prediction in breast ultrasound imaging.

翻译：乳腺超声（BUS）成像中，由于对比度低、存在斑点噪声以及病灶形态多样，准确的肿瘤分割与分类仍具挑战性。本研究提出一种多任务深度学习框架，利用Segment Anything Model（SAM）视觉编码器的嵌入表示，联合执行病灶分割与诊断分类。与基于提示的SAM变体不同，我们的方法采用一种无提示、全监督的适配策略，其中高维SAM特征通过轻量级卷积头或受UNet启发的解码器进行解码，以实现像素级分割。分类分支通过掩码引导的注意力机制得到增强，使模型能够聚焦于病灶相关特征，同时抑制背景伪影。在按类别划分为80%训练集和20%测试集的PRECISE 2025乳腺超声数据集上的实验表明，所提方法实现了0.887的Dice相似系数（DSC）和92.3%的准确率，在PRECISE挑战赛排行榜上名列前茅。这些结果证明，基于SAM的表征与分割引导学习相结合，能显著提升乳腺超声成像中的病灶勾画与诊断预测性能。