用SAM特征实现乳腺超声病灶分割与分类，无需提示词
Prompt-Free SAM-Based Multi-Task Framework for Breast Ultrasound Lesion Segmentation and Classification
- 直接使用SAM视觉编码器特征，不依赖提示词
- 分割Dice达0.887，分类准确率92.3%
- 适合医学图像多任务学习研究者
乳腺超声成像中肿瘤分割与分类因对比度低、斑点噪声和病灶形态多样而困难。本研究提出一种基于Segment Anything Model（SAM）视觉编码器嵌入的多任务深度学习框架，联合完成病灶分割与诊断分类。不同于依赖提示词的SAM变体，该方法采用无提示、全监督适配方式，通过轻量级卷积头或受UNet启发的解码器对高维SAM特征进行像素级分割。分类分支引入掩码引导注意力机制，使模型聚焦病灶特征并抑制背景干扰。在按类别划分、训练占80%、测试占20%的PRECISE 2025乳腺超声数据集上，该方法取得0.887的分割骰子系数（DSC）和92.3%的分类准确率，位居PRECISE挑战赛前列。结果表明，结合分割引导学习的SAM表征显著提升乳腺超声中的病灶边界识别与诊断预测性能。
原文摘要 · Abstract (English)
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.
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