多种深度学习方法已经被提出用于从超声图像中分割乳腺病变。然而，类似的强度分布，可变的肿瘤形态和模糊的边界对乳腺病变分割提出了挑战，特别是对于形状不规则的恶性肿瘤。考虑到超声图像的复杂性，我们开发了一种自适应注意力U-net (AAU-net) 来从超声图像中自动、稳定地分割乳腺病变。具体而言，我们介绍了一个混合自适应注意力模块 (HAAM)，主要由一个通道自注意块和一个空间自注意块组成，来替换传统的卷积操作。与传统卷积操作相比，混合自适应注意力模块的设计可以帮助我们在不同接受域下捕捉更多的特征。与现有的注意力机制不同，HAAM模块可以引导网络自适应地选择更加稳健的通道和空间维度的表示来处理更加复杂的乳腺病变分割。多项实验表明，我们的方法在乳腺病变分割上具有更好的性能。此外，鲁棒性分析和外部实验表明，我们所提出的AAU-net在乳腺病变分割中具有更好的泛化性能。此外，HAAM模块可以灵活地应用于现有的网络框架中。源代码可在https://github.com/CGPxy/AAU-net上获取。

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.