乳腺癌作为一种恶性肿瘤疾病，多年来一直保持较高的发病率和死亡率。超声检查是诊断早期乳腺癌的主要方法之一。然而，正确解读乳房超声图像需要具有专业知识和丰富经验的医生花费大量时间。近年来，基于深度学习的方法凭借其强大的拟合能力，在乳腺肿瘤分割和分类方面取得了重大进展。然而，大多数现有方法侧重于单独执行其中一项任务，并且往往无法有效利用具有相当大诊断价值的特定肿瘤相关区域的信息。在本研究中，我们提出了一种具有局部全局特征交互和多个肿瘤区域引导的多任务网络，用于基于乳腺超声的肿瘤分割和分类。具体来说，我们构建了一个并行 CNN 和 Transformer 的双流编码器，以促进局部和全局特征的分层交互和融合。这种架构使每个流能够利用另一个流的优势，同时保留其独特的特征。此外，我们设计了一个多肿瘤区域指导模块，以从空间域明确学习肿瘤内和肿瘤周围区域内的远程非局部依赖性，从而提供有利于分类的可解释线索。两个乳腺超声数据集的实验结果表明，我们的网络在肿瘤分割和分类任务中优于最先进的方法。与第二好的竞争方法相比，我们的网络在大型外部验证数据集上将诊断准确率从 73.64% 提高到 80.21%，这表明了其卓越的泛化能力。

Breast cancer, as a malignant tumor disease, has maintained high incidence and mortality rates over the years. Ultrasonography is one of the primary methods for diagnosing early-stage breast cancer. However, correctly interpreting breast ultrasound images requires massive time from physicians with specialized knowledge and extensive experience. Recently, deep learning-based method have made significant advancements in breast tumor segmentation and classification due to their powerful fitting capabilities. However, most existing methods focus on performing one of these tasks separately, and often failing to effectively leverage information from specific tumor-related areas that hold considerable diagnostic value. In this study, we propose a multi-task network with local-global feature interaction and multiple tumoral region guidance for breast ultrasound-based tumor segmentation and classification. Specifically, we construct a dual-stream encoder, paralleling CNN and Transformer, to facilitate hierarchical interaction and fusion of local and global features. This architecture enables each stream to capitalize on the strengths of the other while preserving its unique characteristics. Moreover, we design a multi-tumoral region guidance module to explicitly learn long-range non-local dependencies within intra-tumoral and peri-tumoral regions from spatial domain, thus providing interpretable cues beneficial for classification. Experimental results on two breast ultrasound datasets show that our network outperforms state-of-the-art methods in tumor segmentation and classification tasks. Compared with the second-best competitive method, our network improves the diagnosis accuracy from 73.64% to 80.21% on a large external validation dataset, which demonstrates its superior generalization capability.